A pyramid-based mass detection scheme based on texture analysis and neural classifier for digital mammograms

In the paper, we proposed a pyramid-based mass detection method based on texture analysis and neural classifier for digital mammograms. The proposed mass detection method is composed of four parts: pyramid decomposition, region of interest (ROI) selection, feature extraction and neural classifier. Based on pyramid decomposition, a coarse-to-fine approach was utilized to achieve mass detection for reducing computational complexity in the proposed scheme. For decreasing computational complexity, ROI selection where a thresholding algorithm and polynomial function fitting were to find the breast area is also exploited to remove nonbreast regions in the proposed scheme. In the texture analysis, the intensity and texture information extracted from spatial and wavelet domains are utilized to analyze each pixel within the ROI. After feature extraction, these extracted texture features are combined with a supervised neural network to detect masses in the ROI. To evaluate the performance of the proposed scheme, the mammograms of 19 patients captured in Taiwan are used for testing. The experimental result shows that ROI selection can localize breast regions well for further analysis. In addition, the average recall rate of our proposed scheme is more than 86%. Therefore, these experimental results demonstrate that the proposed pyramid-based scheme can achieve mass detection.