HYBRID COMPRESSION OF MEDICAL IMAGES BASED ON LAPPED BIORTHOGONAL TRANSFORM & DISCRETE COSINE TRANSFORM

Abstract

This paper describes a new hybrid image compression technique based on Lapped Biorthogonal Transform (LBT) and Discrete Cosine Transform (DCT) for medical images. The implementation consists of image partitioning module, average gray level estimator, Region of Interest (ROI) extractor, gray level comparator and transformation module. The medical image is partitioned into 8x8 blocks and gray level estimator calculates average gray level for each block of an input image. A threshold has been devised to partition the image into ROI and non-ROI parts by using gray level comparator. Further, we have used LBT to ROI part and DCT to non-ROI part, in implementing our proposed hybrid technique. Results show that better Peak Signal to Noise Ratio (PSNR) with acceptable Compression Ratio (CR) has been achieved using hybrid scheme based on DCT-LBT as compared to the DCT-Wavelet hybrid scheme and conventional DCT or LBT individually.