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.
Highlights
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 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 proposed ICRODI, in this paper is motivated by the work by Jiyo et al (2016),[12] for the automatic segmentation mainly for bone critically from the CT-images used in the diagnostic of spine disorders
Summary
Perform global image thresholding using global Otsu method a. [Vertex, Boundary]←Compute Alpha shape of point set (X, R ), X= coordinate, R= Probe radius. True compression of images using wavelets b. Brain-MRI images for RODI is approximately 620% which is quite acceptable as lossless image quality. The method proposed here can be optimized further to evalaute the bit per pixel to meet the channel contrainst so that without loosing more significant bit teh data can travelk with the limited bandwidth and can truley satisfy the need to RODI image lossless compression. In order to see its realization in real time syncronized application an approach of field programmable gate array(FPGA) approach is planned in the future work as an extension, where the optimization of power and time will be the prime objective function along with the BPP, PSNR and MSE
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