Provide an approach the Quality of Mammography Images Based on a Combination of Contourlet and Curvelet Techniques

Given that breast cancer has become commonplace, Early computer detection to reduce mortality has become a necessity and a challenge. In this study, a method based on image processing techniques to improve the quality of mammographic images is presented. This research is a medical system that has two stages of preprocessing (Histogram equalization and Size equalization image) and feature extraction (The use of contourlet and Curvelet transformations in mammographic images received from patients include three main categories of morphological and histological features, statistical and frequency.) for improvement and increases diagnostic accuracy. To simulate the proposed method, the MIAS digital mammography screening digital image dataset was used and the extracted feature subset is selected for the classifier input, Finally, appropriate classifications and criteria are used to evaluate the proposed method. In the last part of the simulation, the proposed method based on different classifications was evaluated. The best result on the data set was related to the proposed method. The accuracy of the proposed method was 86.3 and it had better results than other methods.