Automated brain lesion classification method for diffusion-weighted magnetic resonance images

Diffusion-weighted magnetic resonance imaging plays an increasingly important role in the diagnosis of several brain diseases by providing detailed information regarding lesion based on the diffusion of water molecules in brain tissue. Conventionally, the differential diagnosis of brain lesions is p...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohd. Saad, Norhashimah
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/54878/1/NorhashimahMohdSaadPFKE2015.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.54878
record_format	uketd_dc
spelling	my-utm-ep.548782020-11-11T06:18:35Z Automated brain lesion classification method for diffusion-weighted magnetic resonance images 2015-06 Mohd. Saad, Norhashimah TK Electrical engineering. Electronics Nuclear engineering Diffusion-weighted magnetic resonance imaging plays an increasingly important role in the diagnosis of several brain diseases by providing detailed information regarding lesion based on the diffusion of water molecules in brain tissue. Conventionally, the differential diagnosis of brain lesions is performed visually by professional neuroradiologists during a highly subjective, time consuming process. Within this context, this study proposes a new technique for automatically detecting and classifying major brain lesions of four types: acute stroke, chronic stroke, tumor and necrosis. An analytical framework of the brain lesions consists of four stages which are pre-processing, segmentation, features extraction and classification. For segmentation process, adaptive thres holding, gray level co-occurrence matrix, region splitting and merging, semi-automatic region growing, automatic region growing and fuzzy C-means were proposed to segment the lesion region. The algorithm performance was then evaluated using Jaccard index, Dice index, and both false positive and false negative rates. Results demonstrated that automatic region growing offered the best performance for lesion segmentation while acute stroke gave the highest rate with 0.838 Dice index. Next, statistical features were extracted from the region of interest and fed into the rule based classifier designed to the best suit to the lesion’s features. The performance of the classifier was evaluated based on overall accuracy, sensitivity and specificity. The overall accuracy for the classification was 81.3%. In conclusion, the proposed automated brain lesion classification method has the potential to diagnose and classify major brain lesions. 2015-06 Thesis http://eprints.utm.my/id/eprint/54878/ http://eprints.utm.my/id/eprint/54878/1/NorhashimahMohdSaadPFKE2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:95563 phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mohd. Saad, Norhashimah Automated brain lesion classification method for diffusion-weighted magnetic resonance images
description	Diffusion-weighted magnetic resonance imaging plays an increasingly important role in the diagnosis of several brain diseases by providing detailed information regarding lesion based on the diffusion of water molecules in brain tissue. Conventionally, the differential diagnosis of brain lesions is performed visually by professional neuroradiologists during a highly subjective, time consuming process. Within this context, this study proposes a new technique for automatically detecting and classifying major brain lesions of four types: acute stroke, chronic stroke, tumor and necrosis. An analytical framework of the brain lesions consists of four stages which are pre-processing, segmentation, features extraction and classification. For segmentation process, adaptive thres holding, gray level co-occurrence matrix, region splitting and merging, semi-automatic region growing, automatic region growing and fuzzy C-means were proposed to segment the lesion region. The algorithm performance was then evaluated using Jaccard index, Dice index, and both false positive and false negative rates. Results demonstrated that automatic region growing offered the best performance for lesion segmentation while acute stroke gave the highest rate with 0.838 Dice index. Next, statistical features were extracted from the region of interest and fed into the rule based classifier designed to the best suit to the lesion’s features. The performance of the classifier was evaluated based on overall accuracy, sensitivity and specificity. The overall accuracy for the classification was 81.3%. In conclusion, the proposed automated brain lesion classification method has the potential to diagnose and classify major brain lesions.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohd. Saad, Norhashimah
author_facet	Mohd. Saad, Norhashimah
author_sort	Mohd. Saad, Norhashimah
title	Automated brain lesion classification method for diffusion-weighted magnetic resonance images
title_short	Automated brain lesion classification method for diffusion-weighted magnetic resonance images
title_full	Automated brain lesion classification method for diffusion-weighted magnetic resonance images
title_fullStr	Automated brain lesion classification method for diffusion-weighted magnetic resonance images
title_full_unstemmed	Automated brain lesion classification method for diffusion-weighted magnetic resonance images
title_sort	automated brain lesion classification method for diffusion-weighted magnetic resonance images
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	http://eprints.utm.my/id/eprint/54878/1/NorhashimahMohdSaadPFKE2015.pdf
_version_	1747817745644257280

Automated brain lesion classification method for diffusion-weighted magnetic resonance images

Similar Items