An improved method of breast MRI segmentation with simplified K-means clustered images

Authors:
Donghoon Kang;Sung Y. Shin;Chang Oan Sung;Jung Y. Kim;Jeong-Ki Pack;Hyung Do Choi
Affiliations:
South Dakota State University;South Dakota State University;Indiana University Southeast;Utica College;Chungnam National University, Korea;ETRI, Daejeon, Korea
Venue:
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Year:
2011

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Abstract

The segmentation of breast Magnetic Resonance Imaging (MRI) has been a long term challenge due to the fuzzy boundaries among objects, small spots, and irregular object shapes in breast MRI. Even though intensity-based clustering algorithms such as K-means clustering and Fuzzy C-means clustering have been used widely for basic image segmentation, they resulted in complicated patterns for computer aided breast MRI diagnosis. In this paper, we propose a new segmentation algorithm to improve the clustering results from K-means clustering algorithm with breast MRI. The major contribution of the proposed algorithm is that it simplifies breast MRI for the computer aided object analysis without loss of original MRI information. The proposed algorithm follows K-means clustering algorithm and explores neighbors and boundary information to redistribute unexpectedly clustered pixels and merge over-segmented objects from K-means clustering algorithm. We will discuss the results from the proposed algorithm and compare them with the result of K-means clustering algorithm.