This paper presents a computer-aided prescreening and storage system, which automatically prescreens the mass regions from mammograms and based on the results, performs a progressive compression in the storage. This is performed in two subsystems called mass screening subsystem and mass feature reserved compression subsystem. In the first subsystem, breast region is firstly extracted from images, followed by gradient enhancement and median filtering. Then, 19 texture features are calculated from 32*32 pixel blocks on the extracted breast region, and suboptimal feature subset is extracted. Then SVM classifier is employed for classifying the regions into mass, breast without masses and background. In the second subsystem, vector quantization GHNN (grey-based competitive Hopfield neural network) is applied on the three regions with different compression rates according their importance factors so as to reserve important features and simultaneously reduce the size of mammograms for storage efficiency.
['Sheng-Chih Yang', 'Yi-Jhen Lin', 'Pau-Choo Chung', 'Giu-Cheng Hsu', 'Chien-Shen Lo']
Mass screening and feature reserved compression in a computer-aided system for mammograms