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Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.

Mammography is by far the proven method of early detection of breast cancer. However, mammography is not without its problems. It is amongst the most difficult of radiological images to interpret as the images are of low contrast and features indicative of abnormalities are very subtle and minute....

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Main Authors: Ngah, Umi Kalthum, Mat Isa, N A, Mohd Noor, Masriah
Format: Conference or Workshop Item
Language:English
Published: 2003
Subjects:
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Online Access:http://eprints.usm.my/14166/1/automated.pdf
http://eprints.usm.my/14166/
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spelling my.usm.eprints.14166 http://eprints.usm.my/14166/ Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications. Ngah, Umi Kalthum Mat Isa, N A Mohd Noor, Masriah TK1-9971 Electrical engineering. Electronics. Nuclear engineering Mammography is by far the proven method of early detection of breast cancer. However, mammography is not without its problems. It is amongst the most difficult of radiological images to interpret as the images are of low contrast and features indicative of abnormalities are very subtle and minute. In this study, a new method of automated edge detection technique is proposed to detect the abnormalities in a region of interest in a mammogram. 2003 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.usm.my/14166/1/automated.pdf Ngah, Umi Kalthum and Mat Isa, N A and Mohd Noor, Masriah (2003) Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications. In: World Congress On Medical Physics And Biomedical Engineering (WC2003), 24 - 29 August 2003, Sydney, Australia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic TK1-9971 Electrical engineering. Electronics. Nuclear engineering
spellingShingle TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Ngah, Umi Kalthum
Mat Isa, N A
Mohd Noor, Masriah
Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.
description Mammography is by far the proven method of early detection of breast cancer. However, mammography is not without its problems. It is amongst the most difficult of radiological images to interpret as the images are of low contrast and features indicative of abnormalities are very subtle and minute. In this study, a new method of automated edge detection technique is proposed to detect the abnormalities in a region of interest in a mammogram.
format Conference or Workshop Item
author Ngah, Umi Kalthum
Mat Isa, N A
Mohd Noor, Masriah
author_facet Ngah, Umi Kalthum
Mat Isa, N A
Mohd Noor, Masriah
author_sort Ngah, Umi Kalthum
title Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.
title_short Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.
title_full Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.
title_fullStr Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.
title_full_unstemmed Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.
title_sort automated seed-based region growing using the moving k-means clustering for the detection of mammographic microcalcifications.
publishDate 2003
url http://eprints.usm.my/14166/1/automated.pdf
http://eprints.usm.my/14166/
_version_ 1643702586647248896
score 13.160551

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