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Early detection of masses in digitized mammograms using texture features and neuro-fuzzy model
A neuro-fuzzy model for fast detection of candidate circumscribed masses in digitized mammograms is presented. The breast tissue is scanned using variable window size, for each sub-image co-occurrence matrices in different orientations (/spl theta/=0/spl deg/, 45/spl deg/, 90/spl deg/ and 135/spl de...
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creator | Youssry, N. Abou-Chadi, F.E.Z. El-Sayad, A.M. |
description | A neuro-fuzzy model for fast detection of candidate circumscribed masses in digitized mammograms is presented. The breast tissue is scanned using variable window size, for each sub-image co-occurrence matrices in different orientations (/spl theta/=0/spl deg/, 45/spl deg/, 90/spl deg/ and 135/spl deg/) are calculated and texture features are estimated for each co-occurrence matrix, then the features are used to train neuro-fuzzy models. The classification results reach 100% for abnormal cases and 80% for normal ones. |
doi_str_mv | 10.1109/NRSC.2003.1217380 |
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The breast tissue is scanned using variable window size, for each sub-image co-occurrence matrices in different orientations (/spl theta/=0/spl deg/, 45/spl deg/, 90/spl deg/ and 135/spl deg/) are calculated and texture features are estimated for each co-occurrence matrix, then the features are used to train neuro-fuzzy models. The classification results reach 100% for abnormal cases and 80% for normal ones.</description><identifier>ISBN: 9775031753</identifier><identifier>ISBN: 9789775031754</identifier><identifier>DOI: 10.1109/NRSC.2003.1217380</identifier><language>eng</language><publisher>IEEE</publisher><subject>Breast cancer ; Breast tissue ; Cancer detection ; Feature extraction ; Histograms ; Image edge detection ; Image segmentation ; Lesions ; Mammography ; Neoplasms</subject><ispartof>Proceedings of the Twentieth National Radio Science Conference (NRSC'2003) (IEEE Cat. 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No.03EX665)</title><addtitle>NRSC</addtitle><description>A neuro-fuzzy model for fast detection of candidate circumscribed masses in digitized mammograms is presented. The breast tissue is scanned using variable window size, for each sub-image co-occurrence matrices in different orientations (/spl theta/=0/spl deg/, 45/spl deg/, 90/spl deg/ and 135/spl deg/) are calculated and texture features are estimated for each co-occurrence matrix, then the features are used to train neuro-fuzzy models. The classification results reach 100% for abnormal cases and 80% for normal ones.</description><subject>Breast cancer</subject><subject>Breast tissue</subject><subject>Cancer detection</subject><subject>Feature extraction</subject><subject>Histograms</subject><subject>Image edge detection</subject><subject>Image segmentation</subject><subject>Lesions</subject><subject>Mammography</subject><subject>Neoplasms</subject><isbn>9775031753</isbn><isbn>9789775031754</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2003</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkM1KxDAURgMiqOM8gLjJC7TmNm2TLKWMPzAo6Kwd0uamRJpWkhTsPL0jM2dz4Cy-xUfIHbAcgKmHt4_PJi8Y4zkUILhkF-RGCVExDqLiV2Qd4zc7UlYgS3lNvjY6DAs1mLBLbhrpZKnXMWKkbqTG9S65A5pj837qg_aRztGNPU34m-aA1KL-d6R6NHTEOUyZnQ-HhfrJ4HBLLq0eIq7PXpHd02bXvGTb9-fX5nGbOcVSVhu0pS6VxBY1NwBWFG3dGcCqKJFJraqWcaUFIlfSIOesVa2todM1dh3wFbk_zTpE3P8E53VY9ucH-B8K2lRw</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Youssry, N.</creator><creator>Abou-Chadi, F.E.Z.</creator><creator>El-Sayad, A.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2003</creationdate><title>Early detection of masses in digitized mammograms using texture features and neuro-fuzzy model</title><author>Youssry, N. ; Abou-Chadi, F.E.Z. ; El-Sayad, A.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-6def4a498ebea3d11f72b6cd1e524e08a95b039a7ee398de330b9bf61ca6ecc13</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Breast cancer</topic><topic>Breast tissue</topic><topic>Cancer detection</topic><topic>Feature extraction</topic><topic>Histograms</topic><topic>Image edge detection</topic><topic>Image segmentation</topic><topic>Lesions</topic><topic>Mammography</topic><topic>Neoplasms</topic><toplevel>online_resources</toplevel><creatorcontrib>Youssry, N.</creatorcontrib><creatorcontrib>Abou-Chadi, F.E.Z.</creatorcontrib><creatorcontrib>El-Sayad, A.M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Youssry, N.</au><au>Abou-Chadi, F.E.Z.</au><au>El-Sayad, A.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Early detection of masses in digitized mammograms using texture features and neuro-fuzzy model</atitle><btitle>Proceedings of the Twentieth National Radio Science Conference (NRSC'2003) (IEEE Cat. No.03EX665)</btitle><stitle>NRSC</stitle><date>2003</date><risdate>2003</risdate><spage>K2</spage><epage>1</epage><pages>K2-1</pages><isbn>9775031753</isbn><isbn>9789775031754</isbn><abstract>A neuro-fuzzy model for fast detection of candidate circumscribed masses in digitized mammograms is presented. The breast tissue is scanned using variable window size, for each sub-image co-occurrence matrices in different orientations (/spl theta/=0/spl deg/, 45/spl deg/, 90/spl deg/ and 135/spl deg/) are calculated and texture features are estimated for each co-occurrence matrix, then the features are used to train neuro-fuzzy models. The classification results reach 100% for abnormal cases and 80% for normal ones.</abstract><pub>IEEE</pub><doi>10.1109/NRSC.2003.1217380</doi></addata></record> |
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subjects | Breast cancer Breast tissue Cancer detection Feature extraction Histograms Image edge detection Image segmentation Lesions Mammography Neoplasms |
title | Early detection of masses in digitized mammograms using texture features and neuro-fuzzy model |
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