Improvement of Breast Cancer Detection Using Non-subsampled Contourlet Transform and Super-Resolution Technique in Mammographic Images

Introduction Breast cancer is one of the most life-threatening conditions among women. Early detection of this disease is the only way to reduce the associated mortality rate. Mammography is a standard method for the early detection of breast cancer. Today, considering the importance of breast cancer detection, computer-aided detection techniques have been employed to increase the quality of mammographic images and help physicians reduce false positive rate (FPR). Materials and Methods In this study, a method was proposed for improving the quality of mammographic images to help radiologists establish a prompt and accurate diagnosis. The proposed approach included three major parts including pre-processing, feature extraction, and classification. In the pre-processing stage, the region of interest was determined and the image quality was improved by non-subsampled contourlet transform and super-resolution algorithm. In the feature extraction stage, some features of image components were extracted and skewness of each feature was calculated. Finally, a support vector machine was utilized to classify the features and determine the probability of benignity or malignancy of the disease. Results Based on the obtained results using Mammographic Image Analysis Society (MIAS) database, the mean accuracy was estimated at 87.26% and maximum accuracy was 96.29%. Also, the mean and minimum FPRs were estimated at 9.55% and 2.87%, respectively.     Conclusion The results obtained using MIAS database indicated the superiority of the proposed method to other techniques. The reduced FPR in the proposed method was a significant finding in the present article.