Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging

Magnetic Resonance Imaging (MRI) plays an important role in the diagnosis of brain disorders. Stroke is one of the major categories of brain disorders. Recent studies support the notion of stroke as the “time is brain” due to the fact that if the treatment is done within six hours of suffering a str...

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Main Author:	Mohd Noor, Nor Shahirah
Format:	Thesis
Language:	English English
Published:	2020
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/25380/1/Stroke%20Lesion%20Segmentation%20And%20Classification%20For%20Diffusion-Weighted%20Imaging.pdf http://eprints.utem.edu.my/id/eprint/25380/2/Stroke%20Lesion%20Segmentation%20And%20Classification%20For%20Diffusion-Weighted%20Imaging.pdf
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id	my-utem-ep.25380
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) Mohd Noor, Nor Shahirah Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging
description	Magnetic Resonance Imaging (MRI) plays an important role in the diagnosis of brain disorders. Stroke is one of the major categories of brain disorders. Recent studies support the notion of stroke as the “time is brain” due to the fact that if the treatment is done within six hours of suffering a stroke, the patient's life can be saved and the outcome can be improved. Conventionally, the diagnosis of brain stroke is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. Therefore, this study proposes a technique for automatic detection, segmentation and classification of brain stroke lesion from MRI images. The types of stroke lesion are acute hemorrhage stroke, acute ischemic stroke, chronic ischemic stroke and sub-acute ischemic stroke. Diffusion weighted imaging (DWI) sequences from the MRI is chosen for the analysis using machine learning and deep learning techniques. The machine learning technique consists of four stages which are pre-processing, segmentation, features extraction and classification. For segmentation, adaptive thresholding, gray level co-occurrence matrix (GLCM), marker-controlled watershed, fuzzy c-Means (FCM) and kMeans are proposed to segment the stroke region. Statistical features are calculated and fed into several classification techniques, which are the linear discriminant analysis (LDA), support vector machine (SVM), weighted k- Nearest Neighbor (k-NN) and bagged tree classifier. Deep learning using regional convolutional neural network (R-CNN) technique is also proposed in the analysis. The technique consists of four stages which are input image, Region Proposal Network (RPN), Convolutional Neural Network (CNN) features computation and classification. The segmentation performances are evaluated using Jaccard indices, Dice Coefficient, false positive and false negative rates. For classification, the performances are evaluated using accuracy, sensitivity and specificity. Segmentation results demonstrated that k-Means offered the best performance for stroke lesion segmentation while sub-acute ischemic stroke gave the highest rate with 0.85 Dice index. Results demonstrated that support vector machine (SVM) offered the best performance for stroke lesion classification with accuracy 98.5% and average training time is 1.8 second. In conclusion, the proposed stroke classification technique has the potential to diagnose and classify brain stroke lesions.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Noor, Nor Shahirah
author_facet	Mohd Noor, Nor Shahirah
author_sort	Mohd Noor, Nor Shahirah
title	Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging
title_short	Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging
title_full	Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging
title_fullStr	Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging
title_full_unstemmed	Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging
title_sort	stroke lesion segmentation and classification for diffusion-weighted imaging
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electronics and Computer Engineering
publishDate	2020
url	http://eprints.utem.edu.my/id/eprint/25380/1/Stroke%20Lesion%20Segmentation%20And%20Classification%20For%20Diffusion-Weighted%20Imaging.pdf http://eprints.utem.edu.my/id/eprint/25380/2/Stroke%20Lesion%20Segmentation%20And%20Classification%20For%20Diffusion-Weighted%20Imaging.pdf
_version_	1747834113619918848
spelling	my-utem-ep.253802021-10-27T16:16:43Z Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging 2020 Mohd Noor, Nor Shahirah T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Magnetic Resonance Imaging (MRI) plays an important role in the diagnosis of brain disorders. Stroke is one of the major categories of brain disorders. Recent studies support the notion of stroke as the “time is brain” due to the fact that if the treatment is done within six hours of suffering a stroke, the patient's life can be saved and the outcome can be improved. Conventionally, the diagnosis of brain stroke is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. Therefore, this study proposes a technique for automatic detection, segmentation and classification of brain stroke lesion from MRI images. The types of stroke lesion are acute hemorrhage stroke, acute ischemic stroke, chronic ischemic stroke and sub-acute ischemic stroke. Diffusion weighted imaging (DWI) sequences from the MRI is chosen for the analysis using machine learning and deep learning techniques. The machine learning technique consists of four stages which are pre-processing, segmentation, features extraction and classification. For segmentation, adaptive thresholding, gray level co-occurrence matrix (GLCM), marker-controlled watershed, fuzzy c-Means (FCM) and kMeans are proposed to segment the stroke region. Statistical features are calculated and fed into several classification techniques, which are the linear discriminant analysis (LDA), support vector machine (SVM), weighted k- Nearest Neighbor (k-NN) and bagged tree classifier. Deep learning using regional convolutional neural network (R-CNN) technique is also proposed in the analysis. The technique consists of four stages which are input image, Region Proposal Network (RPN), Convolutional Neural Network (CNN) features computation and classification. The segmentation performances are evaluated using Jaccard indices, Dice Coefficient, false positive and false negative rates. For classification, the performances are evaluated using accuracy, sensitivity and specificity. Segmentation results demonstrated that k-Means offered the best performance for stroke lesion segmentation while sub-acute ischemic stroke gave the highest rate with 0.85 Dice index. Results demonstrated that support vector machine (SVM) offered the best performance for stroke lesion classification with accuracy 98.5% and average training time is 1.8 second. In conclusion, the proposed stroke classification technique has the potential to diagnose and classify brain stroke lesions. 2020 Thesis http://eprints.utem.edu.my/id/eprint/25380/ http://eprints.utem.edu.my/id/eprint/25380/1/Stroke%20Lesion%20Segmentation%20And%20Classification%20For%20Diffusion-Weighted%20Imaging.pdf text en public http://eprints.utem.edu.my/id/eprint/25380/2/Stroke%20Lesion%20Segmentation%20And%20Classification%20For%20Diffusion-Weighted%20Imaging.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119718 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Engineering Mohd Saad, Norhashimah 1. Abbasi, S. and Tajeripour, F., 2017. Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing, 219, pp. 526-535. 2. Abdel-Maksoud, E., Elmogy, M. and Al-Awadi, R., 2015. Brain Tumor Segmentation Based on a Hybrid Clustering Technique. 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Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging

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