ICRODI: Image Compression of Region of Diagnostics Interest (RODI) using Layer Segmentation and Wavelet

Abstract

Robotic guided medical system requires efficient mechanism of compression of Region of Diagnostics Interest (RODI) in medical images to overcome the tradeoff among efficiency and time which is a computationally challenging task. This task involves the requirement of suitable noise filtering, segmentation, critical feature selection especially at corners of RODI and encoding process. This paper proposes a framework namely ICRODI to evaluate a hybrid approach of compression for region of diagnostic interest in Brain MRI as well as for rest of the region. The approaches used are median filter, thresholding as pre-processing and fuzzy c-mean clustering, Harris corner detection, s-shape fuzzy for segmentation and feature point selection optimization. Further alpha hull of the convex hull is used for getting the volume of the mass and finally the wavelet co-efficient based compression is applied. The effectiveness of the proposed ICRODI is validated by evaluating MSE and PSNR for both RODI and Non-ROSI. The average value of the PSRN for RODI is found approximately 49 % higher as compared to the non-RODI and MSE of the RODI is reduced by approximately 33% as compared to the non-RODI after simulating the process on a numerical simulation platform. The achieved results are quite promising and could be optimized for the VLSI implementation in future.