Textural Feature Based Classification of Mammogram Images Using ANN

Breast cancer is the most common cancer in women worldwide which leads to death of the patient if not detected early. Breast cancer can be detected early with the help of mammogram images. Mammography is a standard imaging modality for the diagnosis and screening of breast cancer. However, suitable image processing techniques are required to detect such ambiguities. Textural analysis of such image is used to detect the cancer tissues. In this paper, we propose a method using Laws Texture Energy Measure (LTEM) as an approach for breast cancer detection. The LTEM method uses the energy maps of the feature matrix for the calculation of feature vector. A Back-propagation method using Artificial Neural Network (ANN) is used to classify the normal, benign and malignant tissue region. The mammography images are obtained from Mammographic Image Analysis Society (MIAS) database for experimentation and analysis. The proposed method is validated in comparison with different back-propagation algorithms and the results show the superiority of the proposed model.

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