Classifier Based Breast Cancer Segmentation

被引:18
|
作者
Kebede, Samuel Rahimeto [1 ]
Debelee, Taye Girma [1 ]
Schwenker, Friedhelm [2 ]
Yohannes, Dereje [1 ]
机构
[1] Addis Ababa Sci & Technol Univ, Addis Ababa 120611, Ethiopia
[2] Ulm Univ, D-89081 Ulm, Germany
关键词
Breast Cancer; Segmentation; K-Means; SVM; KNN; IOU;
D O I
10.4028/www.scientific.net/JBBBE.47.41
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Breast cancer occurs as a result of erratic growth and proliferation cells that originate in the breast. In this paper, the classifiers were used to identify the abnormalities on mammograms to get the region of interest (ROI). Before classifier based segmentation, noise, pectoral muscles, and tags were removed for a successful segmentation process. Then the proposed approach extracted the brightest regions using modified k-means. From the extracted brightest regions, shape and texture features were extracted and given to classifiers (KNN and SVM) and marked as ROI only those non-overlapping abnormal regions. The ROIs obtained using the proposed classifier-based segmentation algorithm was compared with the ground truth annotated by the radiologists. The datasets used to evaluate the performance of the proposed algorithm was public (MIAS) and local datasets (BGH and DADC).
引用
收藏
页码:41 / 61
页数:21
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