Full Automatic Micro Calcification Detection in Mammogram Images Using Artificial Neural Network and Gabor Wavelets

Nowadays, automatic defect detection in Breast images which obtains from mommogram is very important in many diagnostic and therapeutic applications. This paper introduces a Novel automatic breast abnormality detection method that uses mammogram images to determine any abnormality in breast tissues. Here, has been tried to give clear description from breast tissues using Gabor wavelets, Geometric Moment Invariants(GMIs), energy, entropy, contrast and some other statistic features such as mean, median, variance, correlation, values of maximum and minimum intensity .It is used from a feature selection method to reduce the feature space too. This method uses from neural network to do this classification. The purpose of this project is to classify the breast tissues to normal and abnormal classes automatically, that saves the radiologist time, increases accuracy and yield of diagnosis.

Keywords:

Feature extraction , Kernel F-score featureselection , Gabor wavelets , Geometric moment invariants , Artificial neural network , Tumor detection , Segmentation , Microcalcification , Mammogram