Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578186
Title: An accurate rejection model for false positive reduction of mass localisation in mammogram
Authors: Ashwaq Qasem (UKM)
Siti Norul Huda Sheikh Abdullah (UKM)
Shahnorbanun Sahran (UKM)
Rizuana Iqbal Hussain (UKM)
Fuad Ismail (UKM)
Keywords: Breast cancer
Chan-Vese
Mammogram
MCWS
Rejection model
SVM
Issue Date: Jun-2017
Description: The 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.
News Source: Pertanika Journals
ISSN: 0128-7680
Volume: 25
Pages: 49-62
Publisher: Universiti Putra Malaysia Press
Appears in Collections:Journal Content Pages/ Kandungan Halaman Jurnal

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