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Medical Image Compression Using Roi and Nroi Images Suvetha K1, Sultana Mazher2 1Research Scholar, AMET University, Chennai 2Professor, Presidency College, Chennai Online published on 24 October, 2017. Abstract Advanced medical imaging requires storage space of huge quantities of digitized clinical data. Due to the inhibited bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. Among the existing compression schemes, transform coding is one of the most effective strategies. Image data in spatial domain will be changed into spectral domain after the transformation to attain more compression gains. In our proposed system, separation of the original image in to Region of Interest (ROI) and Non-Region of Interest (NROI) parts is done. Then Lossless compression with Set Partition in Hierarchical Trees (SPIHT) algorithm is used to compress the ROI part and Lossy compression with Haar wavelet transform is used to compress the NON-ROI part. This algorithm gives better Peak Signal Noise Ratio (PSNR)and Bits per pixel (BPP) value for medical images. Top Keywords ROI, NROI, SPIHT and HAAR wavelet. Top | |
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