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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
http://eprints.utm.my/id/eprint/43970/
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spelling my.utm.439702017-07-05T01:41:50Z http://eprints.utm.my/id/eprint/43970/ Kidney abnormality detection and classification using ultrasound vector graphic image analysis 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/43970/5/WanMahaniHafizahPFPSK2013.pdf Wan Mahmud, Wan Mahani Hafiza (2013) Kidney abnormality detection and classification using ultrasound vector graphic image analysis. PhD thesis, Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2013
url http://eprints.utm.my/id/eprint/43970/5/WanMahaniHafizahPFPSK2013.pdf
http://eprints.utm.my/id/eprint/43970/
_version_ 1643651331466985472
score 13.211869

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