What is it about?
In this technique the region which contain the salient details are represented as region of interest(ROI) and region with less important details are represented as non region of interest(NROI). In this medical image is segmented into ROI and NROI region by means of morphological segmentation process. ROI part of the image is transformed into wavelet coefficient using discrete wavelet transform. The result of wavelet transform consist of four subbands corresponds to approximation, horizontal, vertical and diagonal coefficients. These coefficients are rearranged using peano space filling curve. Image coefficients are rearranged by tracing the curve. After the reordering entropy encoding is performed to get compressed part of the ROI region. The image pixels in the NROI regions are decomposed using singular value decomposition (SVD) technique.
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Why is it important?
The storage and transmission of medical image is a major issue, because of its size. Hence compression of medical image is essential one for transmission and storage purpose.
Perspectives
In this article compressed ROI and NROI image are merged to yield compressed output. Better result in terms of CR and PSNR is obtained for ultrasound images.
Sankaragomathi Balasubramanian
National Engineering College
Read the Original
This page is a summary of: Region of Interest Based MRI Brain Image Compression Using Peano Space Filling Curve, Current Signal Transduction Therapy, November 2016, Bentham Science Publishers,
DOI: 10.2174/1574362411666160616124516.
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