Kidney abnormality detection and classification using ultrasound vector graphic image analysis

Ultrasound imaging has been widely used in kidney diagnosis, especially to estimate kidney size, shape and position, and to provide information about kidney function, and to help in diagnosis of structural abnormalities like cysts, stone, and infection. However, the use of ultrasound in kidney diagn...

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Main Author:	Wan Mahmud, Wan Mahani Hafiza
Format:	Thesis
Language:	English
Published:	2013
Subjects:	Q Science (General) QH301 Biology
Online Access:	http://eprints.utm.my/id/eprint/43970/5/WanMahaniHafizahPFPSK2013.pdf
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id	my-utm-ep.43970
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spelling	my-utm-ep.439702017-07-05T01:41:50Z Kidney abnormality detection and classification using ultrasound vector graphic image analysis 2013-07 Wan Mahmud, Wan Mahani Hafiza Q Science (General) QH301 Biology Ultrasound imaging has been widely used in kidney diagnosis, especially to estimate kidney size, shape and position, and to provide information about kidney function, and to help in diagnosis of structural abnormalities like cysts, stone, and infection. However, the use of ultrasound in kidney diagnosis is operator dependent where the images may be interpreted differently depending on operators’ skills and experiences, variations in human perceptions of the images, and differences in features used in diagnosis. Current kidney diagnosis may be improved by implementing automated techniques and computer aided diagnosis systems, but have not been widely explored. Therefore, this study proposed a vector graphic image formation method which enables the ultrasound images to be manipulated for various applications including region of interest (ROI) generation, cysts detection and segmentation and abnormality classification. Automatic kidney ROI generation algorithm able to achieve 89.6% true ROI when tested with 125 kidney images. Besides that, the vector graphic formation helps in detection and segmentation of cysts automatically with high accuracy (true positive area ratio = 0.9584, similarity index = 0.9439, Hausdorff distance = 11.4018) and less execution time (11.4 seconds). Performance evaluation to 50 single cyst images, and 25 multiple cysts images gave accuracy of 92%, and 86.89% respectively. This vector graphic formation also helps in extracting better features that successfully classify kidney ultrasound images into three different groups namely normal, infectious and cystic with testing and validation accuracy of 93.33% and 91.67% respectively (p<0.05). Overall, this study has shown promising results and implementation of these proposed algorithms into current kidney diagnosis technique may help in improving current diagnosis accuracy while reducing human intervention and operator dependency. 2013-07 Thesis http://eprints.utm.my/id/eprint/43970/ http://eprints.utm.my/id/eprint/43970/5/WanMahaniHafizahPFPSK2013.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering Faculty of Biosciences and Medical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	Q Science (General) QH301 Biology
spellingShingle	Q Science (General) QH301 Biology Wan Mahmud, Wan Mahani Hafiza Kidney abnormality detection and classification using ultrasound vector graphic image analysis
description	Ultrasound imaging has been widely used in kidney diagnosis, especially to estimate kidney size, shape and position, and to provide information about kidney function, and to help in diagnosis of structural abnormalities like cysts, stone, and infection. However, the use of ultrasound in kidney diagnosis is operator dependent where the images may be interpreted differently depending on operators’ skills and experiences, variations in human perceptions of the images, and differences in features used in diagnosis. Current kidney diagnosis may be improved by implementing automated techniques and computer aided diagnosis systems, but have not been widely explored. Therefore, this study proposed a vector graphic image formation method which enables the ultrasound images to be manipulated for various applications including region of interest (ROI) generation, cysts detection and segmentation and abnormality classification. Automatic kidney ROI generation algorithm able to achieve 89.6% true ROI when tested with 125 kidney images. Besides that, the vector graphic formation helps in detection and segmentation of cysts automatically with high accuracy (true positive area ratio = 0.9584, similarity index = 0.9439, Hausdorff distance = 11.4018) and less execution time (11.4 seconds). Performance evaluation to 50 single cyst images, and 25 multiple cysts images gave accuracy of 92%, and 86.89% respectively. This vector graphic formation also helps in extracting better features that successfully classify kidney ultrasound images into three different groups namely normal, infectious and cystic with testing and validation accuracy of 93.33% and 91.67% respectively (p<0.05). Overall, this study has shown promising results and implementation of these proposed algorithms into current kidney diagnosis technique may help in improving current diagnosis accuracy while reducing human intervention and operator dependency.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Wan Mahmud, Wan Mahani Hafiza
author_facet	Wan Mahmud, Wan Mahani Hafiza
author_sort	Wan Mahmud, Wan Mahani Hafiza
title	Kidney abnormality detection and classification using ultrasound vector graphic image analysis
title_short	Kidney abnormality detection and classification using ultrasound vector graphic image analysis
title_full	Kidney abnormality detection and classification using ultrasound vector graphic image analysis
title_fullStr	Kidney abnormality detection and classification using ultrasound vector graphic image analysis
title_full_unstemmed	Kidney abnormality detection and classification using ultrasound vector graphic image analysis
title_sort	kidney abnormality detection and classification using ultrasound vector graphic image analysis
granting_institution	Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering
granting_department	Faculty of Biosciences and Medical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/43970/5/WanMahaniHafizahPFPSK2013.pdf
_version_	1747817125881315328

Kidney abnormality detection and classification using ultrasound vector graphic image analysis

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