Medical Image Compression Using DCT based MRG Algorithem

Abstract

Nowadays, there is an increase in the volume of data produced and stored in the medical field. Therefore for the efficient handling of these large data there needs the compression technique to re-explore by considering the algorithm's complexity. In this research work, a narrative medical image compression approach is implanted by means of intelligent techniques and is composed of three main stages like Segmentation, Image compression, and Image decompression. From the start, the division procedure is started by parting the picture's Region of Interest (ROI) and Non-ROI areas by Modified Region Growing (MRG) calculation. Further, for ROI regions, Discrete Cosine Transform (DCT) model and SPHIT encoding method are deployed for compression, whereas the Non-ROI region uses the Discrete Wavelet Transform (DWT) and Merge-based Huffman encoding (MHE) methods for doing compression process. Mainly, this research work employs the optimization concept for the optimal selection of filter coefficients from DWT and DCT approaches. For this purpose, a new Improvised Steering angle and Gear-based ROA (ISG-ROA) is proposed, which is the modification of Rider Optimization Algorithm (ROA). To the last, decompression process is handled by reversing the compression process using the same optimized coefficients. The filter coefficient is adapted to finalize the result with reduced compression Ratio (CR).