Breast segmentation using k-means algorithm with a mixture of gamma distributions

Breast cancer is one of the main causes of death among women worldwide. Mammography is an effective imaging modality for early diagnosis of breast cancer. Understanding the nature of data in breast images is very important for developing a model that fits well the data. Gaussian distribution is widely used for modeling the data in breast images but due to the asymmetric nature of the distribution of gray levels in mammogram, Gamma distribution is more suitable. Exploiting Gamma distribution for modeling the k-mean method, we developed an efficient technique for the segmentation of mammograms. The approach was tested over several images taken from mini-MIAS (Mammogram Image Analysis Society, UK) database. The experimental results on mammogram images using this technique showed improvement in the accuracy of breast segmentation for breast cancer detection.