<![CDATA[Nowadays medical imaging systems tend to have the greatest impact on disease identification, diagnosis, and surgical preparation. At the same time, compression of image avoids data redundancy, reduces bandwidth, etc. This makes the system more peculiar in this field. Three main steps are being used in the proposed paradigm: (a) segmentation, (b) image compression, and (c) image decompression. Image segmentation is the first step, which is attained by the Optimized Active Contour Model (OACM). Using a new Modified marriage in honey bees optimization model (MMBO), the weighting factor and maximum iteration of ACM are fine-tuned. Thereby, the collected input image is differentiated or segmented into two: N-ROI and ROI, respectively. The ROI marked field will indeed be encoded using ISPIHT based lossy compression model, whereas the non-ROI area is encoded using DCT based lossy compression model. In terms of BSC, the outcomes from both the ISPIHT algorithm and the DCT model are merged and the compressed image is its output. Following that, the compressed image will then be subjected to image decompression. This will include bit-stream segregation, which will be processed separately for the ROI and non-ROI regions using both ISPIHT decoder and DCT based decomposition. This process results in the original image. Finally, a comparative evaluation is undergone between the proposed and the existing techniques in terms of PSNR, SSIM, and CR as well.]]>
Copyrights © 2021
