Breast Cancer Tumour Diagnosis from Mammography Images Using Wavelet Transform and Hidden Markov Model
Breast cancer is one of the most important reasons of death within women 15 to 54 years old. A woman dies of breast cancer every 13 minutes and 12.6 percent of women are infected during their life. Although mammography is one of the most effective ways of detecting disease; but still it has deficiencies and limitations. Cancerous lesions may not be diagnosed or non-cancerous lesions are detected as cancer in the interpretation of mammography images. Recently, quality of mammography images is increased and image features are extracted using image processing science in order to help radiologists in detection and diagnosis of cancer masses. This will increase detection speed and accuracy rate. In this study, a new method is proposed for detection of suspicious areas of breast cancer tumoursbased on wavelet and hidden Markov model. According to combination of these two methods, the efficiency has been increased compared to method of previous works. In this research, cancer masses are detected as well as percentage of cancer masses becomes clear; this makes to estimate mass growth rate. In this paper, Markov model with tree structure is used in order to extract statistical properties of wavelet transform components. Markov model has special ability in extracting information related to edges and protruding parts of image context due to its features which can accurately detect cancer areas. In this research we try to estimate the appropriate label (clustering) of pixels from a checked image in order to segment cancer areas. Certain joint distribution is assumed for pixels of a region or class; then, the maximum similarity of different areas of an image under review is checked using ML method. Combination of MIAS database and Paden including 150 images is used in order to test the proposed method. The results indicate that proposed method is more accurate compared to methods that use only wavelet transforms method. Detection rate of proposed method is 96% that is improved 24.5% compared to wavelet transform with detection rate of 71.5 percent.