elysium pro titles with abstracts 2018-19...elysium pro titles with abstracts 2018-19 images are,...

Elysium PRO Titles with Abstracts 2018-19


In this paper we propose the application of a novel associative classifier, the Heaviside's Classifier, for

the early detection of Age-Related Macular Degeneration un retinal fundus images. Retinal fundus

images are, first, processed by a simple method based on the Homomorphic filtering and some basic

mathematical morphology operations; in the second phase we extract relevant features of the images

using the Zernike moments, we also apply a feature selection method to select the best features from

the original features set. The dataset created from the images with the best features are used to train

and test a new classification model whose learning and classification phases are based on the

Heaviside's Function. Experimental results show that our method is capable to achieve an accuracy

value about the 94.12% with a dataset created from images belonging to famous image repositories.

EPRO

BIOM - 001

Detection of Age-Related Macular Degeneration in Fundus Images by an Associative

Classifier.

Lung cancer is one of the leading causes of death worldwide. Several computer-aided diagnosis

systems have been developed to help reduce lung cancer mortality rates. This paper presents a novel

structural co-occurrence matrix (SCM)-based approach to classify nodules into malignant or benign

nodules and also into their malignancy levels. The SCM technique was applied to extract features from

images of nodules and classifying them into malignant or benign nodules and also into their malignancy

levels. The computed tomography exams from the lung image database consortium and image database

resource initiative datasets provide information concerning nodule positions and their malignancy

levels. The SCM was applied on both grayscale and Hounsfield unit images with four filters, to wit,

mean, Laplace, Gaussian, and Sobel filters creating eight different configurations. The classification

stage used three well-known classifiers: multilayer perceptron, support vector machine, and k-nearest

neighbors algorithm and applied them to two tasks: (i) to classify the nodule images into malignant or

benign nodules and (ii) to classify the lung nodules into malignancy levels (1 to 5). The results of this

approach were compared to four other feature extraction methods: gray-level co-occurrence matrix,

local binary patterns, central moments, and statistical moments.

EPRO

BIOM - 002

Health of Things Algorithms for Malignancy Level Classification of Lung Nodules.


Dempster-Shafer evidence theory (DS theory) is widely used in brain magnetic resonance imaging

(MRI) segmentation, due to its efficient combination of the evidence from different sources. In this

paper, an improved MRI segmentation method, which is based on fuzzy c-means (FCM) and DS

theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict

information in the pictures. Then, the neighborhood information and the different influences of spatial

location of neighborhood pixels are taken into consideration to handle the spatial information. Finally,

the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images

and the MRI images illustrate that our proposed method is more effective in image segmentation.

EPRO

BIOM - 003

Improved evidential fuzzy c-means method.

Retinal microaneurysms (MAs) are the earliest clinical sign of diabetic retinopathy disease. Detection

of MAs is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this

paper, a novel and reliable method for automatic detection of MAs in retinal images is proposed. In the

first stage of the proposed method, several preliminary microaneurysm candidates are extracted using

a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to

intensity and shape descriptors, a new set of features based on local convergence index filters is

extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting

classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with

different resolutions and modalities (color and scanning laser ophthalmoscope) using six publicly

available data sets including the retinopathy online challenges (ROC) data set. The proposed method

achieves an average sensitivity score of 0.471 on the ROC data set outperforming state-of-the-art

approaches in an extensive comparison. The experimental results on the other five data sets

demonstrate the effectiveness and robustness of the proposed MAs detection method regardless of

different image resolutions and modalities.

EPRO

BIOM - 004

Retinal Micro aneurysms Detection Using Local Convergence Index Features.


Image segmentation is critical and challenging in computer vision and medical image analysis. Despite

decades of research, existing segmentation algorithms are still subject to typical segmentation

problems, such as over-segmentation, under-segmentation, and non-closed and spurious edges. In this

paper, taking the carpal bones from hand X-ray images as the foreground regions, we propose a

segmentation approach to integrate segmentations from region-based and boundary-based methods to

tackle these typical segmentation problems. First, adaptive local thresholding and adaptive Canny edge

detection are explored to extract foreground regions and the edge map. Second, the integration of the

edge map and foreground regions by XORing is proposed, to tackle the over-segmentation by adding

a background boundary from the edge map near the carpal bone boundary so as to break the connection

between the foreground and the over-segmented background, to handle under-segmentation by adding

a foreground boundary from the edge map near the carpal bone boundary so as to enclose the missing

foreground due to under-segmentation, and to complement non-closed edge and spurious edge from

the edge map through the carpal bone regions from the local adaptive thresholding. Optionally, marker-

controlled watershed segmentation or an active contourbased method is employed to refine the

integrated segmentation.

EPRO

BIOM - 005

Delineation of Carpal Bones from Hand X-Ray Images through Prior Model, and

Integration of Region-Based and Boundary-Based Segmentations.

Single-image super-resolution (SR) reconstruction via sparse representation has recently attracted

broad interest. It is known that a low-resolution (LR) image is susceptible to noise or blur due to the

degradation of the observed image, which would lead to a poor SR performance. In this paper, we

propose a novel robust edge-preserving smoothing SR (REPS-SR) method in the framework of sparse

representation. An EPS regularization term is designed based on gradient-domain-guided filtering to

preserve image edges and reduce noise in the reconstructed image. Furthermore, a smoothing-aware

factor adaptively determined by the estimation of the noise level of LR images without manual

interference is presented to obtain an optimal balance between the data fidelity term and the proposed

EPS regularization term. An iterative shrinkage algorithm is used to obtain the SR image results for

LR images. The proposed adaptive smoothing-aware scheme makes our method robust to different

levels of noise. Experimental results indicate that the proposed method can preserve image edges and

reduce noise and outperforms the current state-of-the-art methods for noisy images.

EPRO

BIOM - 006

Robust Single-Image Super-Resolution Based on Adaptive Edge-Preserving

Smoothing Regularization.


To achieve the better segmentation performance, we propose a unified algorithm for automatic glioma

segmentation. In this paper, we first use spatial fuzzy c-mean clustering to estimate region-of-interest

in multimodal MRI images, and then extract some seed points from there for region growing based on

a new notion “affinity”. In the end, we design a two-step strategy to refine the glioma border with

region merging and improved distance regularization level set method. In BRATS 2015 database, we

evaluate the accuracy and robustness of our method with performance scores, including dice, positive

predictive value (PPV), and sensitivity metrics, as well as Hausdorff and Euclidean distance (HD&ED).

The high metric values (dice = 0.86, PPV = 0.90, and sensitivity = 0.84) and small distance errors (HD

= 14.39 mm and ED = 3.31 mm) indicate a remarkable accuracy. Also, we observe the ranking is No.1

in terms of dice and PPV, comparing with the state-of-the-art methods. In addition, the robustness is

also at a high-level due to the refinement structure. And Spearman's rank coefficient test verities a

significant correlation between the high-grade gliomas and low-grade gliomas. Overall, the proposed

method is effective in segmenting gliomas in multimodal images or flair images, and has the potential

in routine examinations of gliomas in daily clinical practice.

EPRO

BIOM - 007

Glioma Segmentation with a Unified Algorithm in Multimodal MRI Images.

This paper develops a new dimensionality reduction method, named biomimetic uncorrelated locality

discriminant projection (BULDP), for face recognition. It is based on unsupervised discriminant

projection and two human bionic characteristics: principle of homology continuity and principle of

heterogeneous similarity. With these two human bionic characteristics, we propose a novel adjacency

coefficient representation, which does not only capture the category information between different

samples, but also reflects the continuity between similar samples and the similarity between different

samples. By applying this new adjacency coefficient into the unsupervised discriminant projection, it

can be shown that we can transform the original data space into an uncorrelated discriminant subspace.

A detailed solution of the proposed BULDP is given based on singular value decomposition. Moreover,

we also develop a nonlinear version of our BULDP using kernel functions for nonlinear dimensionality

reduction. The performance of the proposed algorithms is evaluated and compared with the state-of-

the-art methods on four public benchmarks for face recognition. Experimental results show that the

proposed BULDP method and its nonlinear version achieve much competitive recognition

performance.

EPRO

BIOM - 008

BULDP: Biomimetic Uncorrelated Locality Discriminant Projection for Feature

Extraction in Face Recognition.


Material decomposition allows the reconstruction of material-specific images in spectral X-ray

imaging, which requires efficient decomposition models. Due to the presence of nonideal effects in X-

ray imaging systems, it is difficult to explicitly estimate the imaging systems for material

decomposition tasks. As an alternative to previous empirical material decomposition methods, we

investigated material decomposition using ensemble learning methods in this paper. Three ensemble

methods with two decision trees as the base learning algorithms were investigated to perform material

decomposition in both simulation and experiment. The results were quantitatively evaluated for

comparison studies. In general, the results demonstrate that the proposed ensemble learning methods

often outperform their base learning algorithms, and rarely reduce performance. Compared to the

reference methods and its base learning algorithm, the performance of the Boosting method using

REPTree with regularization is improved by over 42% and 13%, respectively, in the noiseless

simulated scenario of the XCAT phantom with cardiac and respiratory motion, and over 36% and 17%,

respectively, in the noisy scenario. Simultaneously, the performance is improved by over 9% and 8%,

respectively, in the original torso phantom scenario, and over 13% and 12%, respectively, in the

denoising scenario. The results indicate that ensemble learning with gradient descent optimization

algorithms is more appropriate for material decomposition tasks.

EPRO

BIOM - 009

Material Decomposition Using Ensemble Learning for Spectral X-ray Imaging.

In positron emission tomography (PET) image reconstruction, the Bayesian framework with various

regularization terms has been implemented to constrain the radio tracer distribution. Varying the

regularizing weight of a maximum a posteriori (MAP) algorithm specifies a lower bound of the tradeoff

between variance and spatial resolution measured from the reconstructed images. The purpose of this

paper is to build a patch-based image enhancement scheme to reduce the size of the unachievable

region below the bound and thus to quantitatively improve the Bayesian PET imaging. We cast the

proposed enhancement as a regression problem which models a highly nonlinear and spatial-varying

mapping between the reconstructed image patches and an enhanced image patch. An artificial neural

network model named multilayer perceptron (MLP) with backpropagation was used to solve this

regression problem through learning from examples. Using the BrainWeb phantoms, we simulated

brain PET data at different count levels of different subjects with and without lesions. The MLP was

trained using the image patches reconstructed with a MAP algorithm of different regularization

parameters for one normal subject at a certain count level. To evaluate the performance of the trained

MLP, reconstructed images from other simulations and two patient brain PET imaging data sets were

processed.

EPRO

BIOM - 010

Artificial Neural Network Enhanced Bayesian PET Image Reconstruction.


Automated segmentation of fine objects details in a given image is becoming of crucial interest in

different imaging fields. In this paper, we propose a new variational level-set model for both global

and interactive\selective segmentation tasks, which can deal with intensity inhomogeneity and the

presence of noise. The proposed method maintains the same performance on clean and noisy vector-

valued images. The model utilizes a combination of locally computed denoising constrained surface

and a denoising fidelity term to ensure a fine segmentation of local and global features of a given

image. A two-phase level-set formulation has been extended to a multi-phase formulation to

successfully segment medical images of the human brain. Comparative experiments with state-of-the-

art models show the advantages of the proposed method.

EPRO

BIOM - 011

Image Segmentation for Intensity Inhomogeneity in Presence of High Noise.

Optical endomicroscopy (OEM) is an emerging technology platform with preclinical and clinical

imaging applications. Pulmonary OEM via fibre bundles has the potential to provide in vivo, in situ

molecular signatures of disease such as infection and inflammation. However,a enhancing the quality

of data acquired by this technique for better visualization and subsequent analysis remains a

challenging problem. Cross coupling between fiber cores and sparse sampling by imaging fiber bundles

are the main reasons for image degradation, and poor detection performance (i.e., inflammation,

bacteria, etc.). In this paper, we address the problem of deconvolution and restoration of OEM data.

We propose a hierarchical Bayesian model to solve this problem and compare three estimation

algorithms to exploit the resulting joint posterior distribution. The first method is based on Markov

chain Monte Carlo methods, however, it exhibits a relatively long computational time. The second and

third algorithms deal with this issue and are based on a variational Bayes approach and an alternating

direction method of multipliers algorithm, respectively. Results on both synthetic and real datasets

illustrate the effectiveness of the proposed methods for restoration of OEM images.

EPRO

BIOM - 012

Deconvolution and Restoration of Optical Endomicroscopy Images.


Natural image quality assessment (NIQA) wins increasing attention, while NIQA models are rarely

used in the medical community. A couple of studies employ the NIQA methodologies for medical

image quality assessment (MIQA), but building the benchmark data sets necessitates considerable time

and professional skills. In particular, the characteristics of synthesized distortions are different from

those of clinical distortions, which make the results not so convincing. In clinic, signal-to-noise ratio

(SNR) is widely used, which is defined as the quotient of the mean signal intensity measured in a tissue

region of interest (ROI) and the standard deviation of the signal intensity in an air region outside the

imaged object, and both regions are outlined by specialists. We take advantage of the knowledge that

SNR is routinely used and concern whether SNR measure can perform as a baseline metric for the

development of MIQA algorithms. To address the issue, the inter-observer reliability of SNR measure

is investigated regarding to different tissue ROIs [white matter (WM); cerebral spinal fluid (CSF)] in

magnetic resonance (MR) images. A total of 192 T2, 88 T1, 76 T2 and 55 contrast-enhanced T1 (T1C)

weighted images are analyzed. Statistical analysis indicates that SNR values show consistency between

different observers to the same ROI in each modality (Wilcoxon rank sum test, pw ≥ 0.11; and paired

sample t-test, pp 0.28).

EPRO

BIOM - 013

Can Signal-to-Noise Ratio Perform as a Baseline Indicator for Medical Image

Quality Assessment.

X-ray tensor tomography (XTT) is a novel imaging modality for the three-dimensional reconstruction

of X-ray scattering tensors from dark-field images obtained in a grating interferometry setup. The two-

dimensional dark-field images measured in XTT are degraded by noise effects, such as detector readout

noise and insufficient photon statistics, and consequently, the three-dimensional volumes reconstructed

from this data exhibit noise artifacts. In this paper, we investigate the best way to incorporate a

denoising technique into the XTT reconstruction pipeline, i.e., the popular total variation denoising

technique. We propose two different schemes of including denoising in the reconstruction process, one

using a column block-parallel iterative scheme and one using a whole-system approach. In addition,

we compare the results when using a simple denoising approach applied either before or after

reconstruction. The effectiveness is evaluated qualitatively and quantitatively based on datasets from

an industrial sample and a clinical sample. The results clearly demonstrate the superiority of including

denoising in the reconstruction process, along with slight advantages of the whole-system approach.

EPRO

BIOM - 014

Incorporating a Noise Reduction Technique Into X-Ray Tensor Tomography.


Cardiac bi-ventricle segmentation can help physicians to obtain clinical indices, such as mass and

volume of left ventricle (LV) and right ventricle (RV). In this paper, we propose a regression

segmentation framework to delineate boundaries of bi-ventricle from cardiac magnetic resonance (MR)

images by building a regression model automatically and accurately. First, we extract DAISY feature

from images. Then, a point based representation method is employed to depict the boundaries. Finally,

we use DAISY as input and boundary points as labels to train the regression model based on deep

belief network. Regression combined deep learning and DAISY feature can capture high level image

information and accurately segment biventricle with fewer assumptions and lower computational cost.

In our experiment, the performance of the proposed framework is compared with manual segmentation

on 145 clinical subjects (2900 images in total), which are collected from three hospitals affiliated with

two health care centers (London Healthcare Center and St. Josephs HealthCare). The results of our

method and manually segmented method are highly consistent. High Pearson's correlation coefficient

between automated boundaries and manual annotation is up to 0.995 (endocardium of LV), 0.997

(epicardium of LV), and 0.985 (RV). Average Dice metric is up to 0.916 (endocardium of LV), 0.941

(epicardium of LV), and 0.844 (RV). Altogether, experimental results are capable of demonstrating the

efficacy of our regression segmentation framework for cardiac MR images.

EPRO

BIOM - 015

Deep Regression Segmentation for Cardiac Bi-Ventricle MR Images.

Automated 3-D breast ultrasound has been proposed as a complementary modality to mammography

for early detection of breast cancers. To facilitate the interpretation of these images, computer aided

detection systems are being developed in which mass segmentation is an essential component for

feature extraction and temporal comparisons. However, automated segmentation of masses is

challenging because of the large variety in shape, size, and texture of these 3-D objects. In this paper,

the authors aim to develop a computerized segmentation system, which uses a seed position as the only

priori of the problem. A two-stage segmentation approach has been proposed incorporating shape

information of training masses. At the first stage, a new adaptive region growing algorithm is used to

give a rough estimation of the mass boundary. The similarity threshold of the proposed algorithm is

determined using a Gaussian mixture model based on the volume and circularity of the training masses.

In the second stage, a novel geometric edge-based deformable model is introduced using the result of

the first stage as the initial contour. In a data set of 50 masses, including 38 malignant and 12 benign

lesions, the proposed segmentation method achieved a mean Dice of 0.74 ± 0.19 which outperformed

the adaptive region growing with a mean Dice of 0.65 ± 0.2 (p-value <; 0.02).

EPRO

BIOM - 016

Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region

Growing and Supervised Edge-Based Deformable Model.


The use of appearance and shape priors in image segmentation is known to improve accuracy; however,

existing techniques have several drawbacks. For instance, most active shape and appearance models

require landmark points and assume unimodal shape and appearance distributions, and the level set

representation does not support construction of local priors. In this paper, we present novel appearance

and shape models for image segmentation based on a differentiable implicit parametric shape

representation called a disjunctive normal shape model (DNSM). The DNSM is formed by the

disjunction of polytopes, which themselves are formed by the conjunctions of half-spaces. The

DNSM's parametric nature allows the use of powerful local prior statistics, and its implicit nature

removes the need to use landmarks and easily handles topological changes. In a Bayesian inference

framework, we model arbitrary shape and appearance distributions using nonparametric density

estimations, at any local scale. The proposed local shape prior results in accurate segmentation even

when very few training shapes are available, because the method generates a rich set of shape variations

by locally combining training samples. We demonstrate the performance of the framework by applying

it to both 2-D and 3-D data sets with emphasis on biomedical image segmentation applications.

EPRO

BIOM - 017

Image Segmentation Using Disjunctive Normal Bayesian Shape and Appearance

Models.

Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of

the main causes of death in the world. Heart murmurs are the most common abnormal finding when a

patient visits the physician for auscultation. These heart sounds can either be innocent, which are

harmless, or abnormal, which may be a sign of a more serious heart condition. However, the accuracy

rate of primary care physicians and expert cardiologists when auscultating is not good enough to avoid

most of both type-I (healthy patients are sent for echocardiogram) and type-II (pathological patients

are sent home without medication or treatment) errors made. In this paper, the authors present a novel

convolutional neural network based tool for classifying between healthy people and pathological

patients using a neuromorphic auditory sensor for FPGA that is able to decompose the audio into

frequency bands in real time. For this purpose, different networks have been trained with the heart

murmur information contained in heart sound recordings obtained from nine different heart sound

databases sourced from multiple research groups. These samples are segmented and preprocessed using

the neuromorphic auditory sensor to decompose their audio information into frequency bands and, after

that, sonogram images with the same size are generated. These images have been used to train and test

different convolutional neural network architectures. The best results have been obtained with a

modified version of the AlexNet model, achieving 97% accuracy (specificity: 95.12%, sensitivity:

93.20%, and type-II errors.

EPRO

BIOM - 018

Deep Neural Networks for the Recognition and Classification of Heart Murmurs

Using Neuromorphic Auditory Sensors.


The prominent advantage of meshfree method, is the way to build the representation of computational

domain, based on the nodal points without any explicit meshing connectivity. Therefore, meshfree

method can conveniently process the numerical computation inside interested domains with large

deformation or inhomogeneity. In this paper, we adopt the idea of meshfree representation into cardiac

medical image analysis in order to overcome the difficulties caused by large deformation and

inhomogeneous materials of the heart. In our implementation, as element-free Galerkin method can

efficiently build a meshfree representation using its shape function with moving least square fitting,

we apply this meshfree method to handle large deformation or inhomogeneity for solving cardiac

segmentation and motion tracking problems. We evaluate the performance of meshfree representation

on a synthetic heart data and an in-vivo cardiac MRI image sequence. Results showed that the error of

our framework against the ground truth was 0.1189 ± 0.0672 while the error of the traditional FEM

was 0.1793 ± 0.1166. The proposed framework has minimal consistency constraints, handling large

deformation and material discontinuities are simple and efficient, and it provides a way to avoid the

complicated meshing procedures while preserving the accuracy with a relatively small number of

nodes.

EPRO

BIOM - 019

A Meshfree Representation for Cardiac Medical Image Computing.

In this paper, we aim to produce a realistic 2-D projection of the breast parenchymal distribution from

a 3-D breast magnetic resonance image (MRI). To evaluate the accuracy of our simulation, we compare

our results with the local breast density (i.e., density map) obtained from the complementary full-field

digital mammogram. To achieve this goal, we have developed a fully automatic framework, which

registers MRI volumes to X-ray mammograms using a subject-specific biomechanical model of the

breast. The optimization step modifies the position, orientation, and elastic parameters of the breast

model to perform the alignment between the images. When the model reaches an optimal solution, the

MRI glandular tissue is projected and compared with the one obtained from the corresponding

mammograms. To reduce the loss of information during the ray-casting, we introduce a new approach

that avoids resampling the MRI volume. In the results, we focus our efforts on evaluating the agreement

of the distributions of glandular tissue, the degree of structural similarity, and the correlation between

the real and synthetic density maps. Our approach obtained a high-structural agreement regardless the

glandularity of the breast, whilst the similarity of the glandular tissue distributions and correlation

between both images increase in denser breasts. Furthermore, the synthetic images show continuity

with respect to large structures in the density maps.

EPRO

BIOM - 020

Multimodal Breast Parenchymal Patterns Correlation Using a Patient-Specific

Biomechanical Model.


The analysis of gait dynamics is helpful for predicting and improving the quality of life, morbidity, and

mortality in neuro-degenerative patients. Feature extraction of physiological time series and

classification between gait patterns of healthy control subjects and patients are usually carried out on

the basis of 1-D signal analysis. The proposed approach presented in this paper departs itself from

conventional methods for gait analysis by transforming time series into images, of which texture

features can be extracted from methods of texture analysis. Here, the fuzzy recurrence plot algorithm

is applied to transform gait time series into texture images, which can be visualized to gain insight into

disease patterns. Several texture features are then extracted from fuzzy recurrence plots using the gray-

level co-occurrence matrix for pattern analysis and machine classification to differentiate healthy

control subjects from patients with Parkinson's disease, Huntington's disease, and amyotrophic lateral

sclerosis. Experimental results using only the right stride-intervals of the four groups show the

effectiveness of the application of the proposed approach.

EPRO

BIOM - 021

Texture Classification and Visualization of Time Series of Gait Dynamics in Patients

with Neuro-Degenerative Diseases.

The analysis of the pure motion of subnuclear structures without influence of the cell nucleus motion

and deformation is essential in live cell imaging. In this paper, we propose a 2-D contour-based image

registration approach for compensation of nucleus motion and deformation in fluorescence microscopy

time-lapse sequences. The proposed approach extends our previous approach, which uses a static

elasticity model to register cell images. Compared with that scheme, the new approach employs a

dynamic elasticity model for the forward simulation of nucleus motion and deformation based on the

motion of its contours. The contour matching process is embedded as a constraint into the system of

equations describing the elastic behavior of the nucleus. This results in better performance in terms of

the registration accuracy. Our approach was successfully applied to real live cell microscopy image

sequences of different types of cells including image data that was specifically designed and acquired

for evaluation of cell image registration methods. An experimental comparison with the existing

contour-based registration methods and an intensity-based registration method has been performed.

We also studied the dependence of the results on the choice of method parameters.

EPRO

BIOM - 022

Non-Rigid Contour-Based Registration of Cell Nuclei in 2-D Live Cell Microscopy

Images Using a Dynamic Elasticity Model.


Automated optic disk (OD) detection plays an important role in developing a computer aided system

for eye diseases. In this paper, we propose an algorithm for the OD detection based on structured

learning. A classifier model is trained based on structured learning. Then, we use the model to achieve

the edge map of OD. Thresholding is performed on the edge map, thus a binary image of the OD is

obtained. Finally, circle Hough transform is carried out to approximate the boundary of OD by a circle.

The proposed algorithm has been evaluated on three public datasets and obtained promising results.

The results (an area overlap and Dices coefficients of 0.8605 and 0.9181, respectively, an accuracy of

0.9777, and a true positive and false positive fraction of 0.9183 and 0.0102) show that the proposed

method is very competitive with the state-of-the-art methods and is a reliable tool for the segmentation

of OD.

EPRO

BIOM - 023

Optic Disk Detection in Fundus Image Based on Structured Learning.

Objective: Diabetic retinopathy (DR) is characterized by the progressive deterioration of retina with

the appearance of different types of lesions that include micro-aneurysms, hemorrhages, exudates, etc.

Detection of these lesions plays a significant role for early diagnosis of DR. Methods: To this aim, this

paper proposes a novel and automated lesion detection scheme, which consists of the four main steps:

vessel extraction and optic disc removal, preprocessing, candidate lesion detection, and postprocessing.

The optic disc and the blood vessels are suppressed first to facilitate further processing. Curvelet-based

edge enhancement is done to separate out the dark lesions from the poorly illuminated retinal

background, while the contrast between the bright lesions and the background is enhanced through an

optimally designed wideband bandpass filter. The mutual information of the maximum matched filter

response and the maximum Laplacian of Gaussian response are then jointly maximized. Differential

evolution algorithm is used to determine the optimal values for the parameters of the fuzzy functions

that determine the thresholds of segmenting the candidate regions. Morphology-based postprocessing

is finally applied to exclude the falsely detected candidate pixels. Results and Conclusions: Extensive

simulations on different publicly available databases highlight an improved performance over the

existing methods with an average accuracy of 97.71 % and robustness in detecting the various types of

DR lesions irrespective of their intrinsic properties.

EPRO

BIOM - 024

Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy.


Low-dose computed tomography (LDCT) images are often highly degraded by amplified mottle noise

and streak artifacts. Maintaining image quality under low-dose scan protocols is a well-known

challenge. Recently, sparse representation-based techniques have been shown to be efficient in

improving such CT images. In this paper, we propose a 3D feature constrained reconstruction (3D-

FCR) algorithm for LDCT image reconstruction. The feature information used in the 3D-FCR

algorithm relies on a 3D feature dictionary constructed from available high quality standard-dose CT

sample. The CT voxels and the sparse coefficients are sequentially updated using an alternating

minimization scheme. The performance of the 3D-FCR algorithm was assessed through experiments

conducted on phantom simulation data and clinical data. A comparison with previously reported

solutions was also performed. Qualitative and quantitative results show that the proposed method can

lead to a promising improvement of LDCT image quality.

EPRO

BIOM - 025

3D Feature Constrained Reconstruction for Low-Dose CT Imaging.

This study explored the hidden biomedical information from knee MR images for osteoarthritis (OA)

prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative

locations on tibiofemoral cartilage compartment from 3D MR imaging and used PCA analysis to

process the feature set. Four machine learning methods (artificial neural network (ANN), support

vector machine (SVM), random forest and naïve Bayes) were employed to predict the progression of

OA, which was measured by change of Kellgren and Lawrence (KL) grade, Joint Space Narrowing on

Medial compartment (JSM) grade and Joint Space Narrowing on Lateral compartment (JSL) grade. To

examine the different effect of medial and lateral informative locations, we have divided the 36-

dimensional feature set into 18-dimensional medial feature set and 18-dimensional lateral feature set

and run the experiment on four classifiers separately. Experiment results showed that the medial feature

set generated better prediction performance than the lateral feature set, while using the total 36-

dimensional feature set generated the best. PCA analysis is helpful in feature space reduction and

performance improvement. For KL grade prediction, the best performance was achieved by ANN with

AUC = 0.761 and F-measure = 0.714. For JSM grade prediction, the best performance was achieved

by random forest with AUC = 0.785 and F-measure = 0.743, while for JSL grade prediction, the best

performance was achieved by the ANN with AUC = 0.695 and Fmeasure = 0.796. As experiment

results showing that the informative locations on medial compartment provide more distinguishing

features than informative locations on lateral compartment.

EPRO

BIOM - 026

A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine

Learning Methods.


We propose a novel approach to identify one of the most significant dermoscopic criteria in the

diagnosis of cutaneous Melanoma: the blue-whitish structure (BWS). In this paper, we achieve this

goal in a Multiple Instance Learning (MIL) framework using only image-level labels indicating

whether the feature is present or not. To this aim, each image is represented as a bag of (non-

overlapping) regions where each region may or may not be identified as an instance of BWS. A

probabilistic graphical model [1] is trained (in MIL fashion) to predict the bag (image) labels. As

output, we predict the classification label for the image (i.e., the presence or absence of BWS in each

image) and as well we localize the feature in the image. Experiments are conducted on a challenging

dataset with results outperforming state-of-the-art techniques, with BWS detection besting competing

methods in terms of performance. This study provides an improvement on the scope of modelling for

computerized image analysis of skin lesions. In particular, it propounds a framework for identification

of dermoscopic local features from weakly-labelled data.

EPRO

BIOM - 027

Learning to Detect Blue-white Structures in Dermoscopy Images with Weak

Supervision.

Fixed-pattern noise seriously affects the clinical application of optical coherence tomography (OCT),

especially, in the imaging of tumorous tissue. We propose a Hough transform-based fixed-pattern noise

reduction (HTFPNR) method to reduce the fixed-pattern noise for optimizing imaging of tumorous

tissue with OCT system. Using by the HTFPNR method, we detect and map the outline of fixed-pattern

noise in the OCT images, and finally efficiently reduce the fixed-pattern noise by the longitudinal and

horizontal intelligent processing procedure. We adopt the image-to-noise ratio with full information

(INRfi) and the noise reduction ratio (NRR) to evaluate the outcome of fixed-pattern noise reduction

ratio, respectively. The INRfi of OCT image’s noise reduction of ex vivo brainstem tumor is

approximate 21.92 dB. Six groups of OCT images including three types of fixed-pattern noises have

been validated via experimental evaluation of the ex vivo gastric tumor. In the different types of fixed-

pattern noise, the mean INRfis are 25.24 dB, 23.04 dB and 19.35 dB, respectively. This result

demonstrates that it is highly efficient and useful in fixed-pattern noise reduction. The fluctuating range

of the NRR is 0.84-0.88 for three types of added noise in the OCT images. This result demonstrates

that the HTFPNR method can as possible as save useful information by comparing to previous research.

This proposed HTFPNR method can be used into the fixed-pattern noise reduction of OCT images in

other soft biological tissue in the future.

EPRO

BIOM - 028

Optimized Optical Coherence Tomography Imaging with Hough Transform-based

Fixed-pattern Noise Reduction.


The glomerular filtration rate (GFR) is a crucial index to measure renal function. In daily clinical

practice, the GFR can be estimated using the Gates method, which requires the clinicians to define the

region of interest (ROI) for the kidney and the corresponding background in dynamic renal

scintigraphy. The manual placement of ROIs to estimate the GFR is subjective and labor-intensive,

however, making it an undesirable and unreliable process. This work presents a fully automated ROI

detection method to achieve accurate and robust GFR estimations. After image preprocessing, the ROI

for each kidney was delineated using a shape prior constrained level set (spLS) algorithm and then the

corresponding background ROIs were obtained according to the defined kidney ROIs. In computer

simulations, the spLS method had the best performance in kidney ROI detection compared with the

previous threshold method (Threshold) and the Chan-Vese level set (cvLS) method. In further clinical

applications, 223 sets of 99mTc-diethylenetriaminepentaacetic acid (99mTc-DTPA) renal

scintigraphic images from patients with abnormal renal function were reviewed. Compared with the

former ROI detection methods (Threshold and cvLS), the GFR estimations based on the ROIs derived

by the spLS method had the highest consistency and correlations (r=0.98, p<0.001) with the reference

estimated by experienced physicians.

EPRO

BIOM - 029

Automated Region of Interest Detection Method in Scintigraphic Glomerular

Filtration Rate Estimation.

In recent years, retinal vessel segmentation technology has become an important component for disease

screening and diagnosing in clinical medicine. However, retinal vessel segmentation is a challenging

task due to complex distribution of blood vessels, relatively low contrast between target and

background, and potential presence of illumination and pathologies. In this paper, we propose an

automatic retinal vessel segmentation network using deep supervision and smoothness regularization,

which integrates holistically-nested edge detector (HED) and global smoothness regularization from

conditional random ?elds (CRFs). It is an end-to-end and pixel-to-pixel deep convolutional network,

can perform better results than HED-based methods and the methods where CRF inference is applied

as a post-processing method. With co-constraints between pixels, the proposed DSSRN obtains better

results. Finally, we show that our proposed method obtains a sate-of-the-art vessel segmentation

performance on all three benchmarks, DRIVE, STARE and CHASE DB1.

EPRO

BIOM - 030

Automatic Retinal Vessel Segmentation via Deeply Supervised and Smoothly

Regularized Network.


In this article, a hybrid image denoising algorithm based on directional diffusion is proposed.

Specifically, we developed a new noise-removal model by combining the modified isotropic diffusion

(ID) model and the modified Perona-Malik (PM) model. The novel hybrid model can adapt the

diffusion process along the tangential direction of edges in the original image via a new control function

based on the patch similarity modulus. In addition, the patch similarity modulus is used as the new

structure indicator for the modified Perona-Malik model. The feature of second order directional

derivative of edge’s tangential direction allows the proposed model to reduce the aliasing and the noise

around edge during edge preserving smoothing. The proposed method is thus able to efficiently

preserve the edges, textures, thin lines, weak edges and fine details, meanwhile preventing the staircase

effects. Computer experiments on synthetic image and nature images demonstrate that the proposed

model achieves a better performance than the conventional partial differential equations (PDEs) models

and some recent advanced models.

EPRO

BIOM - 031

A Hybrid Model for Image Denoising Combining Modified Isotropic Diffusion

Model and Modified Perona-Malik Model.

Predicting malignant potential is one of the most critical components of a computer-aided diagnosis

(CAD) system for gastrointestinal stromal tumors (GISTs). These tumors have been studied only on

the basis of subjective computed tomography (CT) findings. Among various methodologies, radiomics

and deep learning algorithms, specifically convolutional neural networks (CNNs), have recently been

confirmed to achieve significant success by outperforming the state-of-the-art performances in medical

image pattern classification and have rapidly become leading methodologies in this field. However,

the existing methods generally use radiomics or deep convolutional features independently for pattern

classification, which tend to take into account only global or local features, respectively. In this paper,

we introduce and evaluate a hybrid structure that includes different features selected with radiomics

model and CNN and integrates these features to deal with GIST classification. Radiomics model and

CNN architecture are constructed for global radiomics and local convolutional feature selections,

respectively. Subsequently, we utilize distinct radiomics and deep convolutional features to perform

pattern classification for GIST. Specifically, we propose a new pooling strategy to assemble the deep

convolutional features of 54 3D patches from the same case and integrate these features with the

radiomics features for independent case, followed by random forests (RF) classifier. Our method can

be extensively evaluated using multiple clinical datasets.

EPRO

BIOM - 032

Pattern Classification for Gastrointestinal Stromal Tumors by Integration of

Radiomics and Deep Convolutional Features.


Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies

and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners and

color charts make lesion segmentation an intricate task. In order to detect the lesion in the presence of

these problems, we propose a supervised saliency detection method tailored for dermoscopic images

based on the discriminative regional feature integration (DRFI). DRFI method incorporates multi-level

segmentation, regional contrast, property, background descriptors, and a random forest regressor to

create saliency scores for each region in the image. In our improved saliency detection method, mDRFI,

we have added some new features to regional property descriptors. Also, in order to achieve more

robust regional background descriptors, a thresholding algorithm is proposed to obtain a new pseudo-

background region. Findings reveal that mDRFI is superior to DRFI in detecting the lesion as the

salient object in dermoscopic images. The proposed overall lesion segmentation framework uses

detected saliency map to construct an initial mask of the lesion through thresholding and post-

processing operations. The initial mask is then evolving in a level set framework to fit better on the

lesion's boundaries. The results of evaluation tests on three public datasets show that our proposed

segmentation method outperforms the other conventional state-of-the-art segmentation.

EPRO

BIOM - 033

Supervised Saliency Map Driven Segmentation of Lesions in Dermoscopic Images.

Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma,

pathological myopia, age-related macular degeneration and diabetic retinopathy. With the development

of computer science, computer aided diagnosis has been developed to process and analyse the retinal

images automatically. One of the challenges in the analysis is that the quality of the retinal image is

often degraded. For example, a cataract in human lens will attenuate the retinal image, just as a cloudy

camera lens which reduces the quality of a photograph. It often obscures the details in the retinal images

and posts challenges in retinal image processing and analysing tasks. In this paper, we approximate the

degradation of the retinal images as a combination of human-lens attenuation and scattering. A novel

structure-preserving guided retinal image filtering (SGRIF) is then proposed to restore images based

on the attenuation and scattering model. The proposed SGRIF consists of a step of global structure

transferring and a step of global edge-preserving smoothing. Our results show that the proposed SGRIF

method is able to improve the contrast of retinal images, measured by histogram flatness measure,

histogram spread and variability of local luminosity. In addition, we further explored the benefits of

SGRIF for subsequent retinal image processing and analysing tasks. In the two applications of deep

learning based optic cup segmentation and sparse learning based cup-to-disc ratio (CDR) computation

EPRO

BIOM - 034

Structure-preserving Guided Retinal Image Filtering and Its Application for Optic

Disc Analysis.


Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic

screening methods firstly segment the main structure, and subsequently calculate the clinical

measurement for detection and screening of glaucoma. However, these measurement-based methods

rely heavily on the segmentation accuracy, and ignore various visual features. In this paper, we

introduce a deep learning technique to gain additional image-relevant information, and screen

glaucoma from the fundus image directly. Specifically, a novel Disc-aware Ensemble Network

(DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context

of the global fundus image and the local optic disc region. Four deep streams on different levels and

modules are respectively considered as global image stream, segmentation-guided network, local disc

region stream, and disc polar transformation stream. Finally, the output probabilities of different

streams are fused as the final screening result. The experiments on two glaucoma datasets (SCES and

new SINDI datasets) show our method outperforms other state-of-the-art algorithms.

EPRO

BIOM - 035

Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image.

Recent studies show that pulmonary vascular diseases may specifically affect arteries or veins through

different physiologic mechanisms. To detect changes in the two vascular trees, physicians manually

analyze the chest computed tomography (CT) image of the patients in search of abnormalities. This

process is time-consuming, difficult to standardize and thus not feasible for large clinical studies or

useful in real-world clinical decision making. Therefore, automatic separation of arteries and veins in

CT images is becoming of great interest, as it may help physicians accurately diagnose pathological

conditions. In this work, we present a novel, fully automatic approach to classifying vessels from chest

CT images into arteries and veins. The algorithm follows three main steps: first, a scale-space particles

segmentation to isolate vessels; then a 3D convolutional neural network (CNN) to obtain a first

classification of vessels; finally, graph-cuts (GC) optimization to refine the results. To justify the usage

of the proposed CNN architecture, we compared different 2D and 3D CNNs that may use local

information from bronchus- and vessel-enhanced images provided to the network with different

strategies. We also compared the proposed CNN approach with a Random Forests (RF) classifier. The

methodology was trained and evaluated on the superior and inferior lobes of the right lung of eighteen

clinical cases with non-contrast chest CT scans, in comparison with manual classification.

EPRO

BIOM - 036

Pulmonary Artery-Vein Classification in CT Images Using Deep Learning.


Increasing the image quality of positron emission tomography (PET) is an essential topic in the PET

community. For instance, thin pixelated crystals have been used to provide high spatial resolution

images but at the cost of sensitivity and manufacture expense. In this study, we proposed an approach

to enhance the PET image resolution and noise property for PET scanners with large pixelated crystals.

To address the problem of coarse blurred sinograms with large parallax errors associated with large

crystals, we developed a data-driven, single-image super-resolution (SISR) method for sinograms,

based on the novel deep residual convolutional neural network (CNN). Unlike the CNN-based SISR

on natural images, periodically padded sinogram data and dedicated network architecture were used to

make it more efficient for PET imaging. Moreover, we included the transfer learning scheme in the

approach to process cases with poor labeling and small training data set. The approach was validated

via analytically simulated data (with and without noise), Monte Carlo simulated data, and pre-clinical

data. Using the proposed method, we could achieve comparable image resolution and better noise

property with large crystals of bin sizes 4 times of thin crystals with a bin size from 1×1 mm2 to 1.6×1.6

mm2. Our approach uses external PET data as the prior knowledge for training and does not require

additional information during inference.

EPRO

BIOM - 037

Enhancing the image quality via transferred deep residual learning of coarse PET

sonograms.

The Gabor filter (GF) has been proved to show good spatial frequency and position selectivity, which

makes it a very suitable solution for visual search, object recognition, and, in general, multimedia

processing applications. GFs prove useful also in the processing of medical imaging to improve part

of the several filtering operations for their enhancement, denoising, and mitigation of artifact issues.

However, the good performances of GFs are compensated by a hardware complexity that traduces in

a large amount of mapped physical resources. This paper presents three different designs of a GF,

showing different tradeoffs between accuracy, area, power, and timing. From the comparative study,

it is possible to highlight the strength points of each one and choose the best design. The designs have

been targeted to a Xilinx field-programmable gate array (FPGA) platform and synthesized to 90-nm

CMOS standard cells. FPGA implementations achieve a maximum operating frequency among the

different designs of 179 MHz, while 350 MHz is obtained from CMOS synthesis. Therefore, 86 and

168 full-HD (1920 x 1080) f/s could be processed, with FPGA and std_cell implementations,

respectively. In order to meet space constraints, several considerations are proposed to achieve an

optimization in terms of power consumption, while still ensuring real-time performances.

EPRO

BIOM - 038

Design of a Gabor Filter HW Accelerator for Applications in Medical Imaging.


We investigate the use of deep neural networks (DNNs) for suppressing off-axis scattering in

ultrasound channel data. Our implementation operates in the frequency domain via the short-time

Fourier transform. The inputs to the DNN consisted of the separated real and imaginary components

(i.e. inphase and quadrature components) observed across the aperture of the array, at a single

frequency and for a single depth. Different networks were trained for different frequencies. The output

had the same structure as the input and the real and imaginary components were combined as complex

data before an inverse short-time Fourier transform was used to reconstruct channel data. Using

simulation, physical phantom experiment, and in vivo scans from a human liver, we compared this

DNN approach to standard delay-and-sum (DAS) beamforming and an adaptive imaging technique

that uses the coherence factor (CF). For a simulated point target, the side lobes when using the DNN

approach were about 60 dB below those of standard DAS. For a simulated anechoic cyst, the DNN

approach improved contrast ratio (CR) and contrast-to-noise (CNR) ratio by 8.8 dB and 0.3 dB,

respectively, compared to DAS. For an anechoic cyst in a physical phantom, the DNN approach

improved CR and CNR by 17.1 dB and 0.7 dB, respectively. For two in vivo scans, the DNN approach

improved CR and CNR by 13.8 dB and 9.7 dB, respectively. We also explored methods for examining

how the networks in this work function.

EPRO

BIOM - 039

Deep Neural Networks for Ultrasound Beamforming.

This paper introduces a computer-aided kidney shape detection method suitable for volumetric (3D)

ultrasound images. Using shape and texture priors, the proposed method automates the process of

kidney detection, which is a problem of great importance in computer-assisted trauma diagnosis. This

paper introduces a new complex-valued implicit shape model which represents the multi-regional

structure of the kidney shape. A spatially aligned neural network classifiers with complex-valued

output is designed to classify voxels into background and multi-regional structure of the kidney shape.

The complex values of the shape model and classification outputs are selected and incorporated in a

new similarity metric such the shape-to-volume registration process only fits the shape model on the

actual kidney shape in input ultrasound volumes. The algorithm's accuracy and sensitivity are evaluated

using both simulated and actual 3D ultrasound images, and it is compared against the performance of

the state-of-the-art. The results support the claims about accuracy and robustness of the proposed

kidney detection method, and statistical analysis validates its superiority over state-of-the-art.

EPRO

BIOM - 040

Kidney Detection in 3D Ultrasound Imagery Via Shape to Volume Registration

Based on Spatially Aligned Neural Network.


Optical Coherence Tomography (OCT) is becoming one of the most important modalities for the

noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases,

automating the OCT image analysis is becoming increasingly relevant. In this paper, we propose a

surrogate-assisted classification method to classify retinal OCT images automatically based on

convolutional neural networks (CNNs). Image denoising is first performed to reduce the noise.

Thresholding and morphological dilation are applied to extract the masks. The denoised images and

the masks are then employed to generate a lot of surrogate images, which are used to train the CNN

model. Finally, The prediction for a test image is determined by the average of the outputs from the

trained CNN model on the surrogate images. The proposed method has been evaluated on different

databases. The results (AUC of 0.9783 in the local database and AUC of 0.9856 in the Duke database)

show that the proposed method is a very promising tool for classifying the retinal OCT images

automatically.

EPRO

BIOM - 041

Surrogate-assisted Retinal OCT Image Classification Based on Convolutional Neural

Networks.

Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate

diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and

reduce the risk of disability or even death. Understanding the location and size of infarcts plays a

critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions

are laborious and timeconsuming. In this paper, we propose a novel automatic method to segment acute

ischemic stroke from diffusion weighted images (DWI) using deep 3D convolutional neural networks

(CNNs). Our method can efficiently utilize 3D contextual information and automatically learn very

discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very

deep 3D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of

information and gradients throughout the network. We train our model with Dice objective function to

combat the severe class imbalance problem in data. A DWI dataset containing 242 subjects (90 for

training, 62 for validation and 90 for testing) with various types of acute ischemic stroke was

constructed to evaluate our method. Our model achieved high performance on various metrics (Dice

similarity coefficient: 79.13%, lesion-wise precision: 92.67%, lesion-wise F1 score: 89.25%),

outperforming other state-of-the-art CNN methods by a large margin.

EPRO

BIOM - 042

Automatic Segmentation of Acute Ischemic Stroke from DWI using 3D Fully

Convolutional DenseNets.


Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic

medical image segmentation. However, they have not demonstrated sufficiently accurate and robust

results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the

lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address these

problems, we propose a novel deep learning-based interactive segmentation framework by

incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose

image-specific fine-tuning to make a CNN model adaptive to a specific test image, which can be either

unsupervised (without additional user interactions) or supervised (with additional scribbles). We also

propose a weighted loss function considering network and interaction-based uncertainty for the fine-

tuning. We applied this framework to two applications: 2D segmentation of multiple organs from fetal

Magnetic Resonance (MR) slices, where only two types of these organs were annotated for training;

and 3D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema)

from different MR sequences, where only the tumor core in one MR sequence was annotated for

training.

EPRO

BIOM - 043

Interactive Medical Image Segmentation using Deep Learning with Image-specific

Fine-tuning.

The author introduces a contour detection method that has relatively low complexity yet still highly

accurate. The method is based on extrema detection along the four principal orientations, a trick that

can be used to detect not only edges but, in particular, also ridges and rivers. The author makes a

comparison to the popular Canny algorithm and shows that the proposed method's only downside is

that it cannot detect very high curvatures in edge contours. The method is applied to the task of image

classification (satellite images, Caltech-101, etc.) and it is demonstrated that the use of all three contour

types (edges, ridges, and rives) improves classification accuracy as opposed to the use of only edge

contours. Thus, for image classification, it is more important to extract multiple contour features; the

use of the exact detection method appears to play a smaller role. The author's simple method is also

appealing for use in individual frames, due to its low complexity.

EPRO

BIOM - 044

Rapid contour detection for image classification.


Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR)

plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic

segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most

existing methods segment them separately, and rely on hand-crafted visual feature from fundus images.

In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC

segmentation jointly in a one-stage multilabel system. The proposed M-Net mainly consists of multi-

scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The

multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The

U-shape convolutional network is employed as the main body network structure to learn the rich

hierarchical representation, while the side-output layer acts as an early classifier that produces a

companion local prediction map for different scale layers. Finally, a multi-label loss function is

proposed to generate the final segmentation map. For improving the segmentation performance further,

we also introduce the polar transformation, which provides the representation of the original image in

the polar coordinate system.

EPRO

BIOM - 045

Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and

Polar Transformation.

Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate

diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and

reduce the risk of disability or even death. Understanding the location and size of infarcts plays a

critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions

are laborious and time consuming. In this paper, we propose a novel automatic method to segment

acute ischemic stroke from diffusion weighted images (DWI) using deep 3D convolutional neural

networks (CNNs). Our method can efficiently utilize 3D contextual information and automatically

learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of

training very deep 3D CNN, we equip our network with dense connectivity to enable the unimpeded

propagation of information and gradients throughout the network. We train our model with Dice

objective function to combat the severe class imbalance problem in data. A DWI dataset containing

242 subjects (90 for training, 62 for validation and 90 for testing) with various types of acute ischemic

stroke was constructed to evaluate our method. Our model achieved high performance on various

metrics (Dice similarity coefficient: 79.13%, lesion-wise precision: 92.67%, lesion-wise F1 score:

89.25%), outperforming other state-of-the-art CNN methods by a large margin.

EPRO

BIOM - 046

Automatic Segmentation of Acute Ischemic Stroke from DWI using 3D Fully

Convolutional DenseNets.


Malignant skin lesions are among the most common types of cancer, and automated systems for their

early detection are of fundamental importance. We propose SDI+, an unsupervised algorithm for the

segmentation of skin lesions in dermoscopic images. It is articulated into three steps, aimed at

extracting preliminary information on possible confounding factors, accurately segmenting the lesion,

and post-processing the result. The overall method achieves high accuracy on dark skin lesions and

can handle several cases where confounding factors could inhibit a clear understanding by a human

operator. We present extensive experimental results and comparisons achieved by the SDI+ algorithm

on the ISIC 2017 dataset, highlighting the advantages and disadvantages.

EPRO

BIOM - 047

SDI+: a Novel Algorithm for Segmenting Dermoscopic Images.

A troublesome disease in which damages of the optic nerve of eye's is nothing but the glaucoma, which

causes irretrievable loss of vision. Glaucoma is a disease where if treatment is get late, the person can

blind. Normally glaucoma detects when there is an increase in the fluid in the front of eye. When that

extra fluid is increased, the pressure in your eye is also getting increased. Accordingly, the size of the

optic disc and optic cup is increased as a result diameter also increased. The ratio of the cup and disc

diameter is called cup-to-disc ratio (CDR). Threshold type segmentation method is used in this system

for localizing the optic disc and optic cup. Another edge detection and ellipse fitting algorithm are also

used. The proposed system for optic disc and optic cup localization and CDR calculation is MATLAB

GUI software.

EPRO

BIOM - 048

Glaucoma Detection from Fundus Images Using MATLAB GUI.


The classification of medical images and illustrations from the biomedical literature is important for

automated literature review, retrieval and mining. Although deep learning is effective for large-scale

image classification, it may not be the optimal choice for this task as there is only a small training

dataset. We propose a combined deep and handcrafted visual feature (CDHVF) based algorithm that

uses features learned by three fine-tuned and pre-trained deep convolutional neural networks (DCNNs)

and two handcrafted descriptors in a joint approach. We evaluated the CDHVF algorithm on the

ImageCLEF 2016 Subfigure Classification dataset and it achieved an accuracy of 85.47%, which is

higher than the best performance of other purely visual approaches listed in the challenge leaderboard.

Our results indicate that handcrafted features complement the image representation learned by DCNNs

on small training datasets and improve accuracy in certain medical image classification problems.

EPRO

BIOM - 049

Classification of Medical Images in the Biomedical Literature by Jointly Using Deep

and Handcrafted Visual Features.

Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to

its time-consuming process, there has been a great demand for automatic estimation. However, the

automated analysis of ultrasound images is complicated because they are patient-specific, operator-

dependent, and machine-specific. Among various types of fetal biometry, the accurate estimation of

abdominal circumference (AC) is especially difficult to perform automatically because the abdomen

has low contrast against surroundings, non-uniform contrast, and irregular shape compared to other

parameters. We propose a method for the automatic estimation of the fetal AC from 2D ultrasound data

through a specially designed convolutional neural network (CNN), which takes account of doctors'

decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed

method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein)

and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound

data acquired from 56 pregnant women. Experimental results show that, with relatively small training

samples, the proposed CNN provides sufficient classification results for AC estimation through the

Hough transformation. The proposed method automatically esti mates AC from ultrasound images.

The method is quantitatively evaluated, and shows stable performance in most cases and even for

ultrasound images deteriorated by shadowing artifacts.

EPRO

BIOM - 050

Automatic Estimation of Fetal Abdominal Circumference from Ultrasound Images.

elysium pro titles with abstracts 2018-19...elysium pro titles with abstracts 2018-19 images are,...

Documents