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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578186
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dc.contributor.authorAshwaq Qasem (UKM)
dc.contributor.authorSiti Norul Huda Sheikh Abdullah (UKM)
dc.contributor.authorShahnorbanun Sahran (UKM)
dc.contributor.authorRizuana Iqbal Hussain (UKM)
dc.contributor.authorFuad Ismail (UKM)
dc.date.accessioned2023-11-06T02:59:01Z-
dc.date.available2023-11-06T02:59:01Z-
dc.date.issued2017-06
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:113370
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578186-
dc.descriptionThe false positive (FP) is an over-segment result where the noncancerous pixel is segmented as a cancer pixel. The FP rate is considered a challenge in localising masses in mammogram images. Hence, in this article, a rejection model is proposed by using a supervised learning method in mass classification such as support vector machine (SVM). The goal of the rejection model which is based on SVM is the reduction of FP rate in segmenting mammogram through the Chan-Vese method, which is initialised by the marker controller watershed (MCWS) algorithm. The MCWS algorithm is utilised for segmentation of a mammogram image. The segmentation is subsequently refined through the Chan-Vese method, followed by the development of the proposed SVM rejection model with different window size as well as its application in eliminating incorrect segmented nodules. The dataset comprised of 57 nodules and 113 non-nodules and the study successfully proved the effectiveness of the SVM rejection model to decrease the FP rate.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/view_archives.php?journal=JST-25-S-6
dc.rightsUKM
dc.subjectBreast cancer
dc.subjectChan-Vese
dc.subjectMammogram
dc.subjectMCWS
dc.subjectRejection model
dc.subjectSVM
dc.titleAn accurate rejection model for false positive reduction of mass localisation in mammogram
dc.typeJournal Article
dc.format.volume25
dc.format.pages49-62
dc.format.issueSpecial Issue
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