A Multi-ROIs Medical Image Compression Algorithm with Edge Feature Preserving
|S.Sangeetha1, M.Manimozhi2, E.Priyanga3, S.Hema4
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Different kinds of medical images have different characteristics. But all of them have a common feature, i.e. useful information is usually gathered and occupied a small area in the image and the useful areas (which are called as regions of interest, i.e. ROI), are compress with low compression ratio and other areas, are compress with high compression ratio to make high density compression. The compressed image is kept with useful information as well as small size. The wavelet-based image encoding can improve the compression rate and the visual quality considerably, and many researchers propose different methods for encoding the wavelet-based images. In this project, the SPIHT (Set Partitioning in Hierarchical Trees) algorithm is an efficient method for segmenting the Region of Interest. The SPIHT algorithm adopts a hierarchical quad-tree data structure on wavelet-transformed image. The algorithm extracts image edge information by using canny operator first, and then divides image into regions-of-interest (ROIs) and non-ROIs. The experimental results show advantages: (1) The algorithm has high compression ratio as well as keeping the quality of ROIs; (2) The algorithm is adaptive and practical, which can be used for remote medical image compression, storage and transfer.