A New Breast Cancer Image Classifier Using Gaussian Mixture Model Based on Histogram and Enhanced Roughness Index

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Li, Zhe
South Dakota State University
Analytical, Diagnostic and Therapeutic Techniques and Equipment; Electrical and Computer Engineering
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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 thesis, a new method based on Gaussian Mixture Model (GMM) to perform the breast tumor classification into two different classes (benign class and malignant class) was proposed. Also a new Enhance Roughness Index (ERI) was developed. In the meanwhile, the two different factors (intensity and irregularity index) as input into GMM are investigated, to compare and show which factor for GMM is better than the other one. This algorithm based on the intensity and irregularity index was applied on the mammograms. Through extensive experiments, the results show that this GMM based method is effective and accurate to perform breast tumor image into different classes. Also with ERI as input, the more accurate performance can be obtained.