Medical image compression using advanced coding technique

Modern medical imaging requires storage of large quantities of digitized clinical data. Due to the constrained bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. Among the existing compression schemes, Integer based discrete cosine transform coding is one of the most effective strategies. Image data in spatial domain is transformed into spectral domain after transformation to attain higher compression gains. Based on the quantization strategy, coefficients of low amplitude in the transformed domain are discarded using a threshold technique: set partitioning in hierarchical trees (SPIHT) where in only significant coefficients are retained to increase the compression ratio without inducing salient distortion. In this paper, we used two advanced coding engines context adaptive variable length coding (CAVLC) and embedded block coding with optimal truncation (EBCOT) to code the significant coefficients. Recording or transmitting the significant coefficients instead of the whole coefficients achieves the goal of compression.. Simulations are carried out on different medical images, which include CT skull, angiogram and MR images. Consequent images demonstrate the performance of two coding engines in terms of PSNR & bpp without perceptible alterations. Simulation results showed that the Integer DCT with SPIHT and CAVLC coding has shown better results compared to JPEG & JPEG2000 schemes. Therefore, our proposed method is found to preserve information fidelity while reducing the amount of data.