autogenous diabetic retinopathy censor for ophthalmologists - akshi

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  • Autogenous Diabetic Retinopathy Censor for Ophthalmologists - AKSHI

    Asiri Wijesinghe University of Colombo School of Computing Sri Lanka

  • Internal Supervisors

    Prof. N. D.Kodikara - Senior Lecturer, University of Colombo School of Computing

    Dr. K. D. Sandaruwan University of Colombo School of Computing

    External Supervisor (Medical)

    Dr. Kapila Banduthilaka (MD, FRCOphth) - Consultant Vitreo Retinal Surgeon National Eye Hospital (Sri-Lanka)

    External Advisor (Medical)

    Mr. Dasantha Fonseka CEO at Vision Care (Pvt) Ltd (Sri-Lanka)

  • Outline

    Introduction

    Objectives

    Design & Implementation

    Evaluation

    Uniqueness/ Innovativeness

    Tools and Technologies

    Future Works

    Recognition

    Q & A

  • Introduction

  • Human Vision System

  • Diabetic Retinopathy

    Non proliferative DR (NPDR) Proliferative DR (PDR)

  • Diagnosing Diabetic Retinopathy (Manual Process)

  • Why Consultants Need an Automated System?

    Evaluation is manually performed by expensive equipment such as HRT or expert ophthalmologists.

    Ophthalmologists have to investigate the large portion of retina at once (complex and time consuming process).

    In many cases ophthalmologists can't identify majority of anomalies at the initial observation.

    Lack of expert consultants/ ophthalmologists to detect such diseases in rural areas.

    Patient must have to wait in a long queue (time consuming).

  • Objectives

    Develop a full fledged autogenous censor for classifying severity level of Diabetic Retinopathy (based on retinal lesions) and detecting retinal vascular network to assessment of vessel tortuousness to identify abnormal vessels in human retina.

  • Design & Implementation

    (Methodology, Features and Functionalities)

    NPDR Approach PDR Approach

  • Classification of Severity Level of Diabetic Retinopathy

    (NPDR Approach)

  • Preprocessing

  • Blood Vessels Localizing & Eliminating

    Blood vessels are also detected when analyzing dark lesions.

    As a result dark lesions (microaneurisms) cant be identified directly.

  • Optic Disc Localizing & Eliminating

    Exudate regions are quite similar to the contrast levels of OD.

    As a result brightest lesions (exudates) cant be identified directly.

  • GLCM Statistical Feature Extraction Approach

    Spatial relationship of pixels can be extracted from GLCM approach.

    Regional texture feature extraction approach.

    This matrix consists of frequencies of gray levels occurring between pixel pairs.

    Represent the given image in 8 levels of gray scales.

  • Classification Using ANN

    Our goal is to generate a model that predicts the class label of test dataset.

    Statistical features are fed as input to the ANN (MLP).

  • Design & Implementation

    (Methodology, Features and Functionalities)

    NPDR Approach PDR Approach

  • Measurement of Vessel Tortuosity to Detect Abnormal

    Vessels (PDR Approach)

  • System Architecture for Abnormal Vessel Detection

  • Preprocessing

  • Vascular Network Detection

    Reconstruct the weak vessels (fill the missing pixels and noise reduction).

    As a result we can obtain conspicuously segmented vasculature network.

  • Detection of Untwisted Vessels

    Detect untwisted vessels from the retinal vascular network to assessment lengthy vessels before arising tortuous regions.

    Tortuosity or twistedness of vessels is one of the initial stages of DR.

    Length of the vessels is abnormally growth due to high blood pressure.

  • Vessel Tortuosity Measurement

    Ref: H.C. Han, Twisted blood vessels: symptoms, etiology and biomechanical mechanisms., Journal of vascular research, vol. 49, no. 3, pp. 18597, Jan. 2012.

  • Evaluation (Methodology, Features and Functionalities)

    NPDR Approach PDR Approach

  • Reference Dataset

    The labeled dataset is retrieved from domain experts (human judges) at National Eye Hospital and Vision Care (Pvt) Ltd.

    Dataset contains current state of DR and related symptoms.

    140 images are used for NPDR approach to train the ANN. 108 for DR related patients

    32 for healthy patients

    rest of the dataset for testing purposes (only 100 images are considered)

    In PDR approach 40 retinal images are utilized for testing purposes.

  • Neural Network Approach for DR Classification

    Measure the performance of MLP classifier for DR detection.

    Cross validation technique is utilized.

    140 data items are utilized to form the NN. 70% (98 samples) of them were utilized to train the network

    15% (21 samples) for validation

    The rest of 15% (21 samples) are utilized for testing

  • Performance Evaluation Using Contingency Matrix

  • Performance Evaluation Using Contingency Matrix

    Measurement Training Validation Testing All

    Precision 0.9733 1 0.9411 0.9719

    Recall 0.9605 0.9375 1 0.9629

    F-measure 0.9668 0.9677 0.9696 0.9673

    Accuracy 0.9489 0.9523 0.9523 0.95

  • Performance Evaluation Using ROC Curves

  • AUC Analysis

    The dataset which we assigned to each class is imbalanced.

    AUC is more robust measurement than accuracy when classes in imbalance situation.

    In this evaluation all ROC curves fitted in the upper left corner.

    Class Training Validation Testing All

    Class1 (Non DR) 0.9667 0.9685 0.9672 0.9658

    Class2 (DR) 0.9662 0.9670 0.9551 0.9651

  • Analysis of Error Histogram of ANN

  • Evaluating Generalization Capability of ANN

  • User Level Evaluation for Severity of DR

    The results of the system were re-evaluated with the dataset which obtained from the Vision Care (Pvt) Ltd.

    Benchmarked 42 images with respect to the symptoms and diagnostic stage of patients.

    The vertical fragments suggest to the out comes of actual human expert results (true class).

    Horizontal fragments suggest to outcomes of our system (predicted class).

    85% average accuracy can be obtained in this model.

  • User Level Evaluation for Severity of DR

    DAG MOD NORM

    DAG 13 1 0

    MOD 2 11 1

    NORM 0 2 12

    True Class

    Predicted Class

    Evaluation Measurements

    DAG MOD NORM

    Precision 0.92 0.88 0.85

    Recall 0.86 0.88 0.92

    F-measure 0.88 0.88 0.88

  • Evaluation (Methodology, Features and Functionalities)

    NPDR Approach PDR Approach

  • User Level Evaluation of Vessel Tortuosity Measurement

    Correct Vessels Incorrect Vessels

    Correct Vessels TP FP

    Incorrect Vessels FN TN

    True Class

    Predicted Class

  • User Level Evaluation of Vessel Tortuosity Measurement

    Sensitivity Specificity Accuracy

    Average 0.85 0.89 0.87

  • Uniqueness/ Innovativeness

  • Feature D. J. Cornforth

    et al. (2014)

    A. K. Ikhar et al.

    (2013)

    A. Salazar et al.

    (2013)

    N. Patton et al.

    (2014)

    M. Garca et

    al. (2012)

    Our Method

    Vascular network detection

    Yes Yes Yes Yes No Yes

    Optic Disc detection No No Yes No No Yes

    Reconstruct the weak vessels (algorithm)

    No No No No No Yes

    Untwisted vessels detection (algorithm)

    No No No No No Yes

    Measuring Vessel tortuosity (algorithm)

    No No No No No Yes

    Microaneurysms detection

    Yes Yes No No No Yes

    Hard & soft exudates detection

    Yes No No No Yes Yes

    Uniqueness/ Innovativeness

  • Feature D. J. Cornforth

    et al. (2014)

    A. K. Ikhar et al.

    (2013)

    A. Salazar et al.

    (2013)

    N. Patton et al.

    (2014)

    M. Garca et

    al. (2012)

    Our Method

    Vascular network elimination/ OD removing process

    No/ No No/ No No/ No No/ No No/ No Yes/ Yes

    Modify threshold value to detect abnormal regions

    No No No No No Yes

    Inform medical notification to patients

    No No No No No Yes

    ANN (MLP model) with GLCM statistical texture features

    No No No No No Yes

    Classification of severity & predict the treatment

    No No No No No Yes

    Uniqueness/ Innovativeness

  • Security

    Only authorized persons can be accessed the internal functionalities of s/w.

    Medical information cannot be accessed to external party.

    All segmented images are encrypted and stored in database.

    Sending the mail in secure connection.

  • Tools and Technologies

    Matlab 2013b

    Visual C++

    JAVA

    MySQL

    NVIDIA toolkit (GPGPU programming)

    Weka tool

    Machine Learning

    Image Processing

    Cryptography

    Parallel Computing

    Statistics

  • Future Works

  • Future Works

    This research can be further extended to detect the glaucoma level of patients.

    This research will be extended to differentiate blood vessels such as healthy and diseased. Blood vessels get swallow and narrow in DR and glaucoma patients

    respectively.

    System will be further upgrading with GNU license to modify the software or use pieces of it in new free progr

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