A novel and robust automatic seed point selection method for breast ultrasound images
Rashid Al Mukaddim, Juan Shan, Irteza Enan Kabir, Abdullah Salmon Ashik, Rasheed Abid, Zhennan Yan, Dimitris Metaxas, Brian S. Garra, Кази Хайрул Ислам, Sheikh Kaisar Alam · 2016
Accurate segmentation of breast lesions is among the several challenges in the development of a fully automatic Computer-Aided Diagnosis system for solid breast mass classification. Many high level segmentation methods rely heavily on proper initialization and the seed point selection is usually the necessary first step. In this paper, a fully automatic and robust seed point selection method is proposed. The method involves a number of processing steps in both space and frequency domain and endeavors to incorporate the breast anatomical knowledge. Using a database of 498 images, we compared the proposed method with two other state-of-the-art methods; the proposed method outperforms both methods significantly with a success rate of 62.85% vs. 44.97% and 13.05% on seed point select.