A new histogram-based breast cancer image classifier using Gaussian mixture model

Authors:
Zhe Li;Sung Shin;Soon I. Jeon;Seong H. Son;Jeong K. Pack
Affiliations:
South Dakota State University, Brookings, SD;South Dakota State University, Brookings, SD;Electronics and Telecommunications, Research Institute (ETRI) Daejeon, South Korea;Electronics and Telecommunications, Research Institute (ETRI) Daejeon, South Korea;Chungnam National University, South Korea
Venue:
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Year:
2012

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Abstract

Early stage breast cancer detection is a critical challenge to improve survive rate, and thus it is extremely important to perform breast tumor image classification. In this paper, we propose a new method based on Gaussian Mixture Model (GMM) to classify one input breast tumor image into two different classes (benign class and malignant class). The main contribution of our proposed approach is to innovatively design the breast tumor image classifier using histogram-based GMM. This paper also represents extensive experimental results using this new method. The results show that this new histogram-GMM-based method is effective and accurate to classify breast tumor images into different classes.