a novel diffusion tensor imaging-based computer...

1

A NOVEL DIFFUSION TENSOR

IMAGING-BASED COMPUTER-AIDED

DIAGNOSTIC SYSTEM FOR

EARLY DIAGNOSIS OF AUTISM

By

Mahmoud Mostapha

Thesis Defense, Master of Science,

Electrical and Computer Engineering Department,

University of Louisville

Email: [email protected]

University of Louisville

mailto:[email protected]

Research Motivation

• Autism spectrum disorders (ASDs) is a group

of lifetime developmental disabilities that are

defined by significant social, communication and

behavioral challenges.

• The Centers for Disease Control and Prevention

(CDC)[1] estimates that one in 68 children has

been diagnosed with ASDs in the United States,

which is approximately 30% greater than previous

estimates reported in 2012 of one in 88 children

• Most children with ASDs are currently diagnosed

after the age of four, despite the fact that ASDs

can be identified as early as age two

2[1] Centers for Disease Control and Prevention (CDC), http://www.cdc.gov/media/releases/2014/p0327-autism-spectrum-disorder.html

Problem and Critical Unmet Need

3

• The American Academy of Pediatrics

(AAP)[1] recommends screening for

developmental disabilities at 9,18 and either

24 or 30 months

• This diagnostic approach is not applicable at

a very early age because it relies on the

observation and assessment of age-

dependent emergent skills in the domains of

communication, social interaction, and play

• There is an urgent need for a non-invasive technology with the capability of

providing new laboratory-based measures that confer an accurate and early

diagnosis of autism

[1] AAP: “The American Academy of Pediatrics . ” http:// http://www.aap.org/

Research Objective

• The ultimate goal of this thesis is to develop a

computer-aided diagnosis (CAD) system for the

accurate and early diagnosis of ASDs using diffusion

tensor imaging (DTI)

4

The Proposed CAD System Framework

5

4D DWI Brain Data

INPUT

Final Assessment

Diagnosis

Control

Autistic

Segmentation

STEP 1

Brain Tissues

Segmentation

Brain Extraction

Classification

STEP 3

CSF

GM

WM

Feature Extraction

STEP 2

Brain cortex

Reconstruction

WM Tracts

Reconstruction

Connectivity

Analysis

Shape

Analysis

Diagnosis and

Brain Mapping

Mismatch

Data Acquisition Challenges

• Challenges of scanning infants:

1. Safety challenges

• RF heating effects

(no sedation and anesthesia)

2. Anatomy challenges

• Smaller structures

(low scan resolution)

3. Behavioral challenges

• Subject cooperation difficult

(short scan time)

• Motion problems

(Bulk motion distortions)

6

Diffusion images are sensitive to a number

of artifacts, which includes:

• Noise/SNR issues

• Eddy-current distortions

• Electromagnetic interference

• Venetian blind artifact

• Slice-Wise Inconsistencies

• partial volume effect

Segmentation Challenges (1)

7










8

b0

b1

b2










9


10










11










12










13

Accurate Segmentation of the infant brain tissue is essential for the

early autism diagnosis CAD system. However, the majority of the

existing techniques are developed to work for adult MR brain images

and fail to accurately segment brain tissues from MR infant images

due to the reduced contrast and higher noise

Adult DWI - Axial Adult DTI- Axial Infant DWI - Axial Infant DTI- Axial


14

Infant brain segmentation in the isointense stage (6-12 months) is

more challenging, in which the signal intensity of the white matter is

increasing during the development due to the myelination and

maturation processes; in this stage, the gray matter has the lowest

signal differentiation with the white matter

Histogram Calculation

Non-Diffusion b0 (T2-weighted) Scan Intensity Histogram of

Different Brain Tissues

DWI Brain Data

15

4D DWI Brain Data

INPUT

Final Assessment

Diagnosis

Control

Autistic

Segmentation

STEP 1

Brain Tissues

Segmentation

Brain Extraction

Classification

STEP 3

CSF

GM

WM

Feature Extraction

STEP 2

Brain cortex

Reconstruction

WM Tracts

Reconstruction

Connectivity

Analysis

Shape

Analysis

Diagnosis and

Brain Mapping

Mismatch

4D DWI Brain Data

b0

b1

b13

b25

Axial CoronalSagittal 3D Volume

Infant Brain Segmentation

17

4D DWI Brain Data

INPUT

Final Assessment

Diagnosis

Control

Autistic

Segmentation

STEP 1

Brain Tissues

Segmentation

Brain Extraction

Classification

STEP 3

CSF

GM

WM

Feature Extraction

STEP 2

Brain cortex

Reconstruction

WM Tracts

Reconstruction

Connectivity

Analysis

Shape

Analysis

Diagnosis and

Brain Mapping

Mismatch

BrainExtraction

1. S. M. Smith, “Fast robust automated brain extraction,” Human Brain Mapping,2002

2. M. Jenkinson et al., “BET2: MR-based estimation of brain, skull and scalpsurfaces,” Human Brain Mapping, 2005

3. D. W. Shattuck and R. M. Leahy, “Brainsuite: an automated cortical surfaceidentification tool,” Medical Image Analysis, 2002

4. F. Shi, et al., “LABEL: Pediatric brain extraction using learning-based meta-algorithm,” NeuroImage, 2012

Brain Segmentation

1. L. Zhang et al., “Spatial–temporal constraint for segmentation of serial infant brain MR images,” MIAR, 2010

2. L. Wang et al., “4Dmulti-modality tissue segmentation of serial infant images,”PLoS One, 2012

3. L. J. Wolff et al., “Differences in White Matter Fiber Tract Development Present From 6 to 24 Months in Infants With Autism,” Psychiatry, 2012

4. L. Wang et al., “Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation,” MICCAI, 2013

Related Work

18

The Limitations of the Existing Techniques

19

Statistical-based

Fixed Atlas-based

Deformable model-based

•Heavily depend on the atlas selection•And registration accuracy •Time consuming

•Very sensitive to the initialization model •and the guiding forces

•Depend only on pre-defined probability models• Intensity sensitive

• Most of the previously described MR infant segmentation techniques fail in

the case of infants in the isointense stage as they depend on T1 or T2 scans

• Curent DTI-based infant brain segmentation techniques suffer from the

following limitations: (i) atlases constructed from multiple modalities,

(ii) using nonlinear registration, and (iii) rely on longitudinal information

The Proposed Segmentation Framework

20


21

DWI Image Acquisition

22[1] IBIS: “Infant brain imaging study. ” http://www.ibisnetwork.org/

b0

b25

• More than 300 Diffusion weighted infant

brain data sets were obtained from the

Infant Brain Imaging Study (IBIS) with 10

data sets manually segmented by an MR

expert

• Diffusion weighted MRI brain scans were

acquired with the following parameters:

• Voxel size: 2mm x 2mm x 2mm

• b values between 0 and 1,000 s/mm2

• Number of gradient directions: 25

• Number of slices: 75–81

• Scan time of 5-6 minutes


23

• In order to perform the required DWI quality control (QC),

DTIprep software[1] was used to automatically detects and removes scan

artifacts, and correct motion and eddy current distortions

• Scans with strong remaining artifacts were eliminated, and resultant data

sets with low number of gradient directions, which will produce DTI

estimates with low SNR, were excluded from any further processing

24

Step 1a – DTIprep Preprocessing

[1] Z. Liu, Y. Wang, G. Gerig, S. Gouttard, R. Tao, T. Fletcher, and M. Styner, “Quality control of diffusion weighted images,” in Proceedings of SPIE Medical Imaging 2000: Image Processing (SPIE’10), 2010, pp. 76280J–76280J.

25

Step 1b – DTI Estimation (1)

S0 S1 S3 Sn-1 Sn

zzzyzx

yzyyyx

xzxyxx

DDD

DDD

DDD

D

DWI Data

Diffusion Tensor Ellipsoid Model

• The weighted linear least square (WLLS) method was used to perform

tensor model estimation from the preprocessed DWI data sets, WLLS

method was preferred because of its ability to provide accurate estimates

with low processing times

• DTI to DWI estimation using WLLS method was carried out using

3D Slicer software[1], and to account for bad tensors, related to any

remaining noise or acquisition artifacts, negative eigenvalues (which are

physically meaningless) were shifted

26

Step 1b – DTI Estimation (2)

[1] A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka, etal.,“3D slicer as an image computing platform for the quantitative imaging network,” Multidisciplinary Respiratory Medicine, vol.30, no. 9, pp. 1323–1341, 2012

DWI

DTI

WLLS Estimation


27

Step 2a – LCDG Model (1)

• We used the Linear Combination of Discrete Gaussians (LCDG) to

accurately estimate the marginal density of intensity distribution for the

brain and non-brain tissues

);( and 0, )( ,1

)|()|()(

,

11

,

1

,,

1

,

qPcc

qwqcqP

np

np

C

k

n,k

K

k

kp

K

k

knkn

K

k

kpp,k,

c

c Θ

Positive mixture Negative mixture

q Intensity (gray) levelKp Numbers of the positive Gaussian componentsKn Numbers of the negative Gaussian componentsC Weights coefficientsΨ Discrete Gaussian term with mean μ and variance σ2

[1] A. El-Baz, A. Elnakib, F. Khalifa, M. A. El-Ghar, P. McClure, A. Soliman, and G. Gimel’farb, “Precise Segmentation of 3D Magnetic Resonance

Angiography,” IEEE Transaction on Biomedical Engineering, vol. 59, no. 7, pp. 2019–2029, 2012.28

Step 2a – LCDG Model (2)

• At the end of this step, a discriminant threshold τ is calculated in a way

that ensures the best separation between the brain and the non-brain

voxel signals

• This threshold will be used in the next steps to enhance the process of

classifying image voxels into either brain or non-brain

29

• Enhance the spatial inhomegeinty by minimizing the distance between

each voxel and its 26-neighbors using 3D generalized Gauss-Markov

random field (GGMRF)[1] algorithm

• The continuity of 𝑞 values of each brain DWI scan is modified by applying

the gradient descent algorithm to search for the closest minimum of the

following equation:

30

Step 2b – GGMRF Model (1)

[1] C. Bouman and K. Sauer, “A generalized gaussian image model for edge preserving MAP estimation,” IEEE Transactions on

Image Processing, vol. 2, no. 3, pp. 296–310, 1993.

ෝ𝑞𝑠 = 𝑎𝑟𝑔 m𝑖𝑛𝑞𝑠

[ |𝑞𝑠 − 𝑞𝑠| + 𝜌𝛼𝜆𝛽

𝑟∊𝑣𝑠

𝜂𝑠, 𝑟 𝑞𝑠 − 𝑞𝑟𝛽 ]

𝑞𝑠 Original gray level 𝑞𝑠 Expected gray level estimate𝑣𝑠 The 26-neighborhood of 3D location 𝑠 = 𝑥, 𝑦, 𝑧𝜂𝑠, 𝑟 GGMRF potential𝜌, λ Scaling factors𝛼,𝛽 GGMRF controlling parameters

• The voxel signals are nudged additionally towards their most appropriate

grouping through incrementing or decrementing them by a bias value of ϵ.

The latter was chosen experimentally as 0.5% of the maximum gray

value, in accord with the discriminant threshold (τ) determined from the

LCDG model

31

Step 2b – GGMRF Model (2)

Intensity Normalization

3D Gibbs Smoothing

3D generalized Gauss-Markov random field

• After the final modified image is obtained, a 3D region growing is

applied, starting from a seed point at the center of the image volume,

followed by connected component analysis to calculate the final brain

mask, which is used to find the final extracted brain

32

Step 2c – Region Growing

Original Brain

Smoothed Brain

Brain mask

Extracted Brain


33

WM

GM

CSF

Others

Va

ria

bil

ity

0%

100%

Step 3 – Initial Segmentation

)(*)|(),( mPmgPmgP sp

NMF-Based Visual Appearance Model

(P(g|m))

Adaptive Shape Model

(Psp(m))

34

• In this thesis, five different anisotropy features were calculated using 3D

Slicer software[1], namely, mean diffusivity (MD), fractional

anisotropy (FA), relative anisotropy (RA), axial diffusivity (λ ), and

radial diffusivity (λ⊥)

35

Step 3a – NMF-Based Model (1)

[1] A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka,et al.,“3D slicer as an image computing platform for the quantitative imaging network,” Multidisciplinary Respiratory Medicine,vol. 30, no. 9, pp. 1323–1341, 2012

=

MD λ λ⊥

FA RA

=

• Nonnegative Matrix Factorization (NMF) is a method for clustering data

by factorizing an input matrix A into a weight matrix W and an output

matrix H such that:

36


I

A

H

N

I J

N

W

J

I The dimensionality of the input data

N The number of input samples

J The dimensionality of the output space

• NMF feature fusion is applied to extract new meaningful features from the

large dimensional DTI feature space (A), which consists of one

appearance feature (b0) and five anisotropy features (FA, RA, MD, λ , λ⊥)

• In the training phase, the weight matrix W and an output matrix H can be

approximated by optimizing:

• The alternating least square (ALS) algorithm was used because of its

high speed and flexibility compared with other competing algorithms

37


=

2

,

2

1minimize WHA

HW

0 subject to W,H

[1] M. W. Berry, M. Browne, A. N. Langville, V. P. Pauca, and R. J. Plemmons, “Algorithms and applications for approximatenonnegative matrix factorization,” Computational Statistics & Data Analysis, 52(1):155–173, 2007.

• Given a weight matrix W learned using NMF, the feature vector of a new

voxel, B, is projected into H-space by using the psuedo-inverse of W

38


• To model the visual appearance of these features, a K-means classifier

was used with the classes J-dimensional centroids 𝐶𝑙; 𝑙 ∊ 𝐿 that were

calculated in the H-space during the training phase

• The NMF-based probabilities for brain label 𝑙 ∊ 𝐿, and voxel (𝑥, 𝑦, 𝑧) ∊ 𝑅 is

defined as:

39


Ll zyxBl

zyxBl

zyx

Hd

HdlmgP

)(

1

)(

1

)|(

,,:

,,:

,,

• Where 𝑑𝑖(𝐻𝐵: 𝑥, 𝑦, 𝑧) is the Euclidian

distance from the vector 𝐻𝐵: 𝑥, 𝑦, 𝑧 to the

centroid of class 𝑙 and 𝐿 is the set of

class labels

• Expected shapes of each brain label are constrained with a adaptive

probabilistic shape prior

• A training set of images, collected from different subjects (10 data sets)

with their new NMF fused features, are co-aligned by 3D affine

transformations with 12 degrees of freedom in a way that maximizes their

Mutual Information (MI)[1]

40

Rzyx

zyxzyxspsp mPmP),,(

,,,,: )()(

Statistical Prior Shapes

WM GMCSF Others

Vari

ab

ilit

y

0%

100%

[1] Viola, P., Wells III, W.M.: “Alignment by Maximization of Mutual Information.” International Journal of Computer Vision, 24(2), 137–154 (1997).

Step 3b – Adaptive Shape Model (1)

Step 3b – Adaptive Shape Model (2)

• Form an initial region map m using the marginal estimated density and

prior shape of each brain label

42

DWI brain images

CSF

GM

WM

)()|(),( mPmgPmgP sp

Step 3c – Initial MAP (1)

• Initial segmentation using the using the constructed adaptive shape

model and the NMF-based visual appearance model

43

Step 3c – Initial MAP (2)

DWI brain images

Initial segmentation

Extracted brain images

Axi

alSa

gitt

alC

oro

nal


44

45

3D Spatial Interaction MGRF Model

(Pv(m))

)(*)(*)|(),( mPmPmgPmgP vsp

NMF-Based Visual Appearance Model

(P(g|m))

Adaptive Shape Model

(Psp(m))

Step 4 – Final Segmentation

Improve the region map m using voxel-wise Bayes classifier

WM

GM

CSF

Others

Va

ria

bil

ity

0%

100%

• Markov Gibbs Random Field (MGRF)[1] with nearest 26-neighbors of the

voxels are used to calculate the 3D pair-wise interactions between region

labels and analytic bi-valued Gibbs potentials, that depend only on

whether the nearest pairs of labels are equal or not

Z Normalization factorNa Nearest 26-neighborhoodV Gibbs potential with an analytical estimate [1] :


3D Neighborhood system

[1] A. El-Baz, “Novel stochastic models for medical image analysis,” Ph.D. dissertation, University of Louisville,Louisville, KY, USA, 2006. 46

R vm ),,( ),,(,,,, ),( 1

)( zyx szyxzyx mmV

v eZ

P

2

12V eq mf

𝑓𝑒𝑞( 𝑚) The relative frequency of equal

labels in all the neighboring voxel pairs

• Construct the second-order Spatial Interaction (MGRF) Model (Pv(m))


47

3D Neighborhood System

GM


48

DWI brain images

Initial segmentation

Extracted brain images

Final segmentation

Axi

alSa

gitt

alC

oro

nal

Performance Evaluation Metrics (1)

The performance was evaluated using three metrics[1]:

a) Dice Similarity Coefficient (DSC)[2]

• The DSC characterizes the agreement between the segmented and

ground truth regions:

• TP True Positive

• TN True Negative

• FP False Positive

• FN False Negative

49

FNFPTP

TPDSC

2

2

[1] Babalola, Kolawole Oluwole, et al. "An evaluation of four automatic methods of segmenting the subcorticalstructures in the brain." Neuroimage 47.4 (2009): 1435-1447.

[2] D. Lee R, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, pp. 297–302, 1945.


b) Modified Hausdorff Distance[1]

• Hausdorff distance (H) from a set S to a set G is defined as the

maximum distance of the set S to the nearest point in the set G:

s points of set S

g points of set G

d(s,g) the Euclidean distance between ‘s’ and ‘g’

• The bidirectional Hausdorff distance, between the segmented

region S and its ground truth G is defined as:

• In this work, the 95-percentile bidirectional Hausdorff distance as a

metric that measures the segmentation accuracy

50

)}},{{min{max),( gsdGSHGgSs

[1] G. Gerig, M. Jomier, and M. Chakos, “Valmet: A new validation tool for assessing and improving 3D object segmentation,” in Medical Image Computing and Computer Assisted Intervention, 2001, pp. 516–523.

S)}H(G,G),max{H(S, G)(S,HBi


c) Absolute Volume Difference (AVD)

• Defined as the ratio of the absolute difference between the ground

truth volume and the segmented volume, to the ground truth volume

51

AVD =|𝑉𝑠−𝑉𝑔|

𝑉𝑔Vs The segmented volumeVg The ground truth volume

MetricDifferent Brain Structures

CSF GM WM Others Brain

DSC (%) 87.96±3.31 89.92±2.86 95.23±1.18 92.81±5.26 96.64±1.15

H95(mm3) 2.42±0.56 1.98±1.07 1.98±0.01 6.60±2.75 7.17±3.96

AVD (%) 6.10±4.70 9.85±2.34 5.15±2.03 9.74±9.65 2.66±2.80

52

Segmentation Results

Infant Brain Segmentation

Infant Brain Extraction

• 3D visualization for our segmentation results:

CSF GM WM

Feature Extraction

53

4D DWI Brain Data

INPUT

Final Assessment

Diagnosis

Control

Autistic

Segmentation

STEP 1

Brain Tissues

Segmentation

Brain Extraction

Classification

STEP 3

CSF

GM

WM

Feature Extraction

STEP 2

Brain cortex

Reconstruction

WM Tracts

Reconstruction

Connectivity

Analysis

Shape

Analysis

Diagnosis and

Brain Mapping

Mismatch

Study Participants

• This study included data from the Infant Brain Imaging Study (IBIS) with

study participants are 6 moth old infants with high risk of developing

ASDs

• Final assessment was made at age 24 months, and based on an

ASDcutoff threshold, the high-risk infants were divided into two groups:

ASD negative (control) and ASD-positive (autistic)

• From 300 subjects provided, only 28 subjects with available final

diagnosis; however 3 subjects were excluded from further processing

owing to high scan artifacts and motion

• The final group of 25 subjects in this study included 6 infants, which met

the criteria for ASDs (4 males and 2 females), and 19 that did not

(11 males and 8 females)

54[1] IBIS: “Infant brain imaging study. ” http://www.ibisnetwork.org/

Feature Extraction Framework

55

Spherical Harmonics Analysis (1)

• The shape analysis was based on spherical harmonic reconstruction,

which considers 3D surface data (i.e., brain cortex) as a linear

combination of specific basis functions, namely spherical harmonics

(SHs)

• The spherical harmonics shape analysis is performed in five steps:

(i) mesh generation, (ii) mesh smoothing, (iii) unit sphere mapping, (iv)

spherical harmonics reconstruction, and (v) shape metrics calculation

56


• To perform a quantitative analysis of the brain shape, two techniques for

measuring the complexity of the cerebral cortex are proposed:

1. SH reconstruction error

• The error between the original cortex mesh nodes and the SH approximated

cortex mesh nodes can be calculated in terms of Euclidean distance

57


2. Surface complexity

• A new metric for examining the complexity of the brain using the SH

coefficients is also proposed:

58

𝑆 𝑓 =

𝑁=0

∞

𝑁 𝐵𝑁2

f Unit SphereN Number of HarmonicsB SH Coefficients

Shape Analysis Results

• The performance of the proposed 3D brain cortex shape analysis

methods were evaluated by applying them on both the control and the

autistic groups:

• These preliminary results show that the proposed 3D brain cortex shape

analysis methods are promising features for accurately discriminating

between autistic and control subjects

59

MetricBrain Class

Autistic Control

SH reconstruction error 233.93±28.01 241.96±37.62

Surface complexity 85.61±1.31 86.09±2.12

Tractography Analysis (1)

• A deterministic tractography approach built in 3D Slicer software[1] was

used to generate the required white matter fiber tracts

• The tractography connectivity analysis is performed in three steps:

(i) fiber orientation extraction, (ii) pathway propagation, (iii) propagation

termination

60

[1] A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka,et al.,“3D slicer as an image computing platform for the quantitative imaging network,” Multidisciplinary Respiratory Medicine,vol. 30, no. 9, pp. 1323–1341, 2012

Tractography Analysis (2)

• After white matter fiber tracts extraction, three DTI measurements were

generated and mapped to for each fiber tract, namely, fractional

anisotropy (FA), axial diffusivity (λ∥), and radial diffusivity (λ⊥)

61

Anisotropic Diffusion in

white matter Fibers

• FA values were generated for each fiber

tract to measure the degree of anisotropy of

local diffusivity:

𝐹𝐴 =3[ 𝜆1− ҧ𝜆 2+ 𝜆2− ҧ𝜆 2+ 𝜆3− ҧ𝜆 2

2 𝜆12+ 𝜆2

2+ 𝜆32

• λ∥ and λ⊥ values, which represent diffusion parallel and

transverse to axonal directions, were also produced:

λ∥= 𝜆1 λ⊥=𝜆2+ 𝜆3

2

Connectivity Analysis Results

• The performance of the proposed connectivity analysis methods were

evaluated by applying them on both the control and the autistic groups

• These preliminary results show relatively higher values of all the

generated DTI measurements in the autistic brains, when compared to

the control brains. These initial results indicates that the extracted DTI

features are promising in differentiating between autistic and control

subjects

62

MetricBrain Class

Autistic Control

Mean Fractional Anisotropy 0.690±0.025 0.672±0.077

Mean Axial Diffusivity 5.290±3.076 4.810±3.957

Mean Radial Diffusivity 1.549±1.074 1.177±0.561

Infant Brain Classification

63

4D DWI Brain Data

INPUT

Final Assessment

Diagnosis

Control

Autistic

Segmentation

STEP 1

Brain Tissues

Segmentation

Brain Extraction

Classification

STEP 3

CSF

GM

WM

Feature Extraction

STEP 2

Brain cortex

Reconstruction

WM Tracts

Reconstruction

Connectivity

Analysis

Shape

Analysis

Diagnosis and

Brain Mapping

Mismatch

Classification (1)

• Based on the five features extracted from the infant DTI data, the

potential of each feature is tested to identify autistic and control subjects

• To perform this task, five k-means classifiers were built, using each

feature, to evaluate the diagnostic capability of each feature

64

Feature Control Autistic Overall

SH Reconstruction Error57.895%

(11 out of 19)50%

(3 out of 6)56%

(14 out of 25)

Surface Complexity42.105%

(8 out of 19)50%

(3 out of 6)44%

(11 out of 25)

Mean Fractional Anisotropy57.895%

(11 out of 19)66.667%

(4 out of 6)60%

(15 out of 25)

Mean Axial Diffusivity84.211%

(16 out of 19)33.333%

(2 out of 6)72%

(18 out of 25)

Mean Radial Diffusivity68.421%

(13 out of 19)33.333%

(2 out of 6)60%

(15 out of 25)

Classification (2)

• The preliminary results based on the available limited data sets

(25 subjects: 6 autistic and 19 control) show that some features are

outstanding candidates to distinguish between the autistic and control

groups

• The mean FA shows a potential to identify the autistic subjects with an

accuracy of 67%, where as the mean axial diffusivity shows a potential

to identify the autistic subjects with an accuracy of 84%

• To build more powerful classifier, our future work includes collecting

additional data and to fuse between the extracted features based on the

developed genetic algorithm proposed by Khalifa et al. [1]

65[1] F. Khalifa, G. Beache, M. Abou El-Ghar, T. El-Diasty, G. Gimelfarb, M. Kong, and A. El-Baz, “Dynamic contrast-enhanced MRI-based early detection of acute renal transplant rejection,” IEEE, 2013.

Thesis Conclusions

66

•This thesis proposes a novel CADsystem for the early diagnosis ofASDs using shape and connectivityfeatures, extracted from DTI images

•The accuracy of the proposed CADsystem has been validated on 25infants with a high risk of developingASDs

•The preliminary diagnostic resultsare promising in identifying autisticfrom control patients at an earlystage

Thesis Main Contributions

67

•A novel infant brain extractionapproach to automatically remove anynon-brain from the input DWI infantbrain images

•A novel atlas-based brain segmentationframework for the automatedsegmentation of different brainstructures from DTI infant brain images

•A novel NMF-based visual appearancemodel that has the ability to model alarge dimensional feature space

•A novel framework for the accurate andearly diagnosis of ASDs using DTI infantbrain images

Thesis Future Work (1)

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•The proposed brain segmentation frameworkwill be extended to include advanced DTIfeatures with adaptive optimal level of NMFdimensionality reduction

•The proposed approach will be used to segmentother brain structures (e.g. Cerebellum,Hippocampus, Corpus Callosum,…)

• In the future, we plan to investigate theutilization of multiple classification featuresderived from spherical harmonics and brainconnectivity to be integrated in the proposedCAD system

•The proposed brain classification framework willbe extended to involve advanced identificationof brain regions that have significant differencesbetween autistic and control subjects usingconstructed brain maps

Thesis Future Work (2)

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•The proposed CAD system for earlydiagnosis of ASDs will be tested on largerdata sets with known ground truths

•The proposed CAD system will be used toexplore longitudinal scans to trackchanges in the brain that are attributed toASDs

•Also will extend the CAD system tocharacterize other brain disorders (e.g.Alzheimer’s, dyslexia,…)

•Another future direction is applying thedeveloped models in other clinicalapplications (e.g., acute renal rejection,lung cancer detection, cancerous cellsdetection in the prostate……)

• At the end, I would like to thank my committee members:

Dr. El-Baz, Dr. Inanc, and Dr. Guru

• Also, I would like to thank all the bioimaging lab members

for their support during my work in this thesis

Acknowledgment

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a novel diffusion tensor imaging-based computer...

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