Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI)

Research and development of brain detection and diagnosis system for brain disorder based on Magnetic Resonance Imaging (MRI) have become one of the most common interest in the past few years. Out of various MRI techniques, Diffusion-Weighted Imaging (DWI) remains the most accurate technique for ear...

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Main Author:	Muda, Ayuni Fateeha
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/20544/1/Brain%20Lesion%20Segmentation%20And%20Classification%20Using%20Diffusion-Weighted%20Imaging%20%28DWI%29.pdf http://eprints.utem.edu.my/id/eprint/20544/2/Brain%20Lesion%20Segmentation%20And%20Classification%20Using%20Diffusion-Weighted%20Imaging%20%28DWI%29.pdf
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id	my-utem-ep.20544
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Mohd Saad, Norhashimah
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Muda, Ayuni Fateeha Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI)
description	Research and development of brain detection and diagnosis system for brain disorder based on Magnetic Resonance Imaging (MRI) have become one of the most common interest in the past few years. Out of various MRI techniques, Diffusion-Weighted Imaging (DWI) remains the most accurate technique for early detection and discrimination of several brain lesions such as stroke. This study proposed the image analysis technique for automatically segmenting and classifying abnormal lesion structures from DWI. Four lesions namely acute stroke, chronic stroke, solid tumor and necrosis were analyzed. The proposed analysis framework were pre-processing, segmentation, features extraction and classification. Four different segmentation techniques were proposed based on Thresholding with Morphological Operation (TMO), Fuzzy C-Means (FCM), Fuzzy C-Means with Active Contour (FCMAC) and Fuzzy C-Means with Correlation Template (FCMCT) to segment the lesion’s region. Next, the statistical parameters from spatial and wavelet transforms were extracted from the Region of Interest (ROI) as features. These features were classified using a rule-based classifier for automatic classification. The results indicate that FCMCT offered the best performance for Jaccard Index, Dice Index, False Positive Rate and False Negative Rate which were 0.6, 0.73, 0.19 and 0.2 respectively. The overall accuracy, sensitivity and specificity for the classification were 89 %, 86 % and 96 %. In conclusion, the proposed hybrid analysis has the potential to be explored as a computer-aided tool to detect and diagnose of human brain lesion.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Muda, Ayuni Fateeha
author_facet	Muda, Ayuni Fateeha
author_sort	Muda, Ayuni Fateeha
title	Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI)
title_short	Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI)
title_full	Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI)
title_fullStr	Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI)
title_full_unstemmed	Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI)
title_sort	brain lesion segmentation and classification using diffusion-weighted imaging (dwi)
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Electronic And Computer Engineering
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/20544/1/Brain%20Lesion%20Segmentation%20And%20Classification%20Using%20Diffusion-Weighted%20Imaging%20%28DWI%29.pdf http://eprints.utem.edu.my/id/eprint/20544/2/Brain%20Lesion%20Segmentation%20And%20Classification%20Using%20Diffusion-Weighted%20Imaging%20%28DWI%29.pdf
_version_	1747833979056160768
spelling	my-utem-ep.205442021-10-08T16:35:57Z Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI) 2016 Muda, Ayuni Fateeha T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Research and development of brain detection and diagnosis system for brain disorder based on Magnetic Resonance Imaging (MRI) have become one of the most common interest in the past few years. Out of various MRI techniques, Diffusion-Weighted Imaging (DWI) remains the most accurate technique for early detection and discrimination of several brain lesions such as stroke. This study proposed the image analysis technique for automatically segmenting and classifying abnormal lesion structures from DWI. Four lesions namely acute stroke, chronic stroke, solid tumor and necrosis were analyzed. The proposed analysis framework were pre-processing, segmentation, features extraction and classification. Four different segmentation techniques were proposed based on Thresholding with Morphological Operation (TMO), Fuzzy C-Means (FCM), Fuzzy C-Means with Active Contour (FCMAC) and Fuzzy C-Means with Correlation Template (FCMCT) to segment the lesion’s region. Next, the statistical parameters from spatial and wavelet transforms were extracted from the Region of Interest (ROI) as features. These features were classified using a rule-based classifier for automatic classification. The results indicate that FCMCT offered the best performance for Jaccard Index, Dice Index, False Positive Rate and False Negative Rate which were 0.6, 0.73, 0.19 and 0.2 respectively. The overall accuracy, sensitivity and specificity for the classification were 89 %, 86 % and 96 %. In conclusion, the proposed hybrid analysis has the potential to be explored as a computer-aided tool to detect and diagnose of human brain lesion. 2016 Thesis http://eprints.utem.edu.my/id/eprint/20544/ http://eprints.utem.edu.my/id/eprint/20544/1/Brain%20Lesion%20Segmentation%20And%20Classification%20Using%20Diffusion-Weighted%20Imaging%20%28DWI%29.pdf text en public http://eprints.utem.edu.my/id/eprint/20544/2/Brain%20Lesion%20Segmentation%20And%20Classification%20Using%20Diffusion-Weighted%20Imaging%20%28DWI%29.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=105836 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Electronic And Computer Engineering Mohd Saad, Norhashimah 1. Adalsteinsson, D. and Sethian, J., 1995. A Fast Level Set Method for Propagating Interfaces. Journal of Computational Physics, 118(2), pp.269-277. 2. Adams, R. and Bischof, L., 1994. Seeded region growing. 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