@inproceedings{73ee6767e450444ca81e4dcf347f39c7,
title = "A novel and robust automatic seed point selection method for breast ultrasound images",
abstract = "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.",
keywords = "Breast-cancer, Computer-aided diagnosis (CAD), Image-segmentation, Quantitative-ultrasound, Ranking-function, Seed-point, Sonography, Tissue-Characterization, ultrasound",
author = "Mukaddim, {Rashid Al} and Juan Shan and Kabir, {Irteza Enan} and Ashik, {Abdullah Salmon} and Rasheed Abid and Zhennan Yan and Metaxas, {Dimitris N.} and Garra, {Brian S.} and Islam, {Kazi Khairul} and Alam, {S. Kaisar}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 1st International Conference on Medical Engineering, Health Informatics and Technology, MediTec 2016 ; Conference date: 17-12-2016 Through 18-12-2016",
year = "2017",
month = jan,
day = "26",
doi = "10.1109/MEDITEC.2016.7835370",
language = "English (US)",
series = "1st International Conference on Medical Engineering, Health Informatics and Technology, MediTec 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "1st International Conference on Medical Engineering, Health Informatics and Technology, MediTec 2016",
address = "United States",
}