Automated breast masses segmentation in digitized mammograms

In this paper, an automated segmentation method is proposed. The method is applied to the segmentation of breast masses in digitized mammograms and it operates on the whole mammograms instead of manually selected regions. Pixels with local maximum gray levels are flagged as seeds, fro...

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Main Authors:	Zhang, Han, Foo, Say Wei, Thng, Choon Hua, Krishnan, Shankar M.
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2009
Online Access:	https://hdl.handle.net/10356/90926 http://hdl.handle.net/10220/4676
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-90926
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spelling	sg-ntu-dr.10356-909262020-03-07T13:24:46Z Automated breast masses segmentation in digitized mammograms Zhang, Han Foo, Say Wei Thng, Choon Hua Krishnan, Shankar M. School of Electrical and Electronic Engineering IEEE International Workshop on BioMedical Circuits & Systems (2004 : Singapore) In this paper, an automated segmentation method is proposed. The method is applied to the segmentation of breast masses in digitized mammograms and it operates on the whole mammograms instead of manually selected regions. Pixels with local maximum gray levels are flagged as seeds, from which many candidate objects are grown using modified region-growing technique. Following which False Positive (FP) reduction using decision tree is applied to discard the normal tissue regions. A total of 40 mammograms from Mammographic Image Analysis Society (MIAS) are analyzed. 36 masses are correctly segmented by the proposed method, resulting in 90% True Positive Rate at 1.3 FPs per image. Published version 2009-07-03T03:25:46Z 2019-12-06T17:56:34Z 2009-07-03T03:25:46Z 2019-12-06T17:56:34Z 2004 2004 Conference Paper Zhang, H., Foo, S. W., Krishnan, S. M., & Thng, C. H. (2004). Automated breast masses segmentation in digitized mammograms. IEEE International Workshop on BioMedical Circuits & Systems (pp. 1-4). https://hdl.handle.net/10356/90926 http://hdl.handle.net/10220/4676 10.1109/BIOCAS.2004.1454102 en © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site. 4 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
description	In this paper, an automated segmentation method is proposed. The method is applied to the segmentation of breast masses in digitized mammograms and it operates on the whole mammograms instead of manually selected regions. Pixels with local maximum gray levels are flagged as seeds, from which many candidate objects are grown using modified region-growing technique. Following which False Positive (FP) reduction using decision tree is applied to discard the normal tissue regions. A total of 40 mammograms from Mammographic Image Analysis Society (MIAS) are analyzed. 36 masses are correctly segmented by the proposed method, resulting in 90% True Positive Rate at 1.3 FPs per image.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Zhang, Han Foo, Say Wei Thng, Choon Hua Krishnan, Shankar M.
format	Conference or Workshop Item
author	Zhang, Han Foo, Say Wei Thng, Choon Hua Krishnan, Shankar M.
spellingShingle	Zhang, Han Foo, Say Wei Thng, Choon Hua Krishnan, Shankar M. Automated breast masses segmentation in digitized mammograms
author_sort	Zhang, Han
title	Automated breast masses segmentation in digitized mammograms
title_short	Automated breast masses segmentation in digitized mammograms
title_full	Automated breast masses segmentation in digitized mammograms
title_fullStr	Automated breast masses segmentation in digitized mammograms
title_full_unstemmed	Automated breast masses segmentation in digitized mammograms
title_sort	automated breast masses segmentation in digitized mammograms
publishDate	2009
url	https://hdl.handle.net/10356/90926 http://hdl.handle.net/10220/4676
_version_	1681047840604815360

Automated breast masses segmentation in digitized mammograms

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