MAMMOGRAM COMPRESSION TECHNIQUES USING HAAR WAVELET AND QUADTREE DECOMPOSITION-BASED IMAGE ENHANCEMENT

Abstract

Biomedical image compression plays an important role in the medical field. Mammograms are medical images used in the early detection of breast cancer. Mammogram image compression is a challenging task because these images contain information that occupies huge size for storage. The aim of image compression is to reduce the image size and the time taken for recovering the original image without any loss. In this paper, two different techniques of mammogram compression are introduced. The proposed algorithm includes two main steps. First, a preprocessing step is applied to enhance the image, and then a compression algorithm is applied to the enhanced image. The algorithm is tested using 322 mammogram images from the online MIAS database. Three parameters are used to evaluate the performance of the compression techniques; compression ratio (CR), Peak Signal to Noise Ratio (PSNR) and processing time. According to the results, Haar wavelet-based compression for enhanced images is better in terms of CR of 26.25% and PSNR of 47.27[Formula: see text]dB.