@inproceedings{f42f40f8bdad4ac5bf3dc559e459d723,
title = "Breast tissue segmentation on MR images using KFCM with spatial constraints",
abstract = "Accurate segmentation of breast on MR images is an essential and crucial step for computer-aided breast disease diagnosis and surgical planning. In this paper, an effective approach is proposed for segmenting the breast image into different regions, each corresponding to a different tissue. The segmentation work flow comprises two key steps. Firstly, we use the threshold-based method and morphological operations to determine the breast-air boundary and breast-chest wall so that the breast region can be extracted. Then a kernelled fuzzy C-means algorithm with spatial information (SKFCM) is used to separate the fibroglandular tissues from the fat. The proposed method is used to segment the clinical breast MR images. Experimental results have been shown visually and achieve reasonable consistency. The SKFCM method is appropriate for the problem of breast tissue segmentation.",
keywords = "Breast MRI, Breast tissue segmentation, SKFCM",
author = "Hong Song and Qian Zhang and Feifei Sun and Jiandong Wang and Quansheng Wang and Jingdan Qiu and Deqiang Kou",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Granular Computing, GrC 2014 ; Conference date: 22-10-2014 Through 24-10-2014",
year = "2014",
month = dec,
day = "11",
doi = "10.1109/GRC.2014.6982845",
language = "English",
series = "Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "254--258",
editor = "Yasuo Kudo and Shusaku Tsumoto",
booktitle = "Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014",
address = "United States",
}