Abstract
Generally lossless compression should be used for ROI (Region of Interest) and lossy compression should be used for ROB (Region of Background) with a lower quality. In existing system, Region of Interest (ROI) is selected manually, but ROI is selected automatically in the proposed method, pre-processing is done to improve the visual quality of the image. Segmentation is carried out accurately and efficiently using region growing followed by morphological processing method. The features are extracted and classification is done in medical image using Fuzzy logic. ROB part of an image is compressed using SPIHT (Set Partition In Hierarchical Tree) algorithm in near lossless manner. Finally the ROI is superimposed in compressed Non ROI (ROB) image. This method improves the compression ratio and increase the PSNR value compared to existing method. The proposed method is used for implementations of teleradiology and digital picture archiving and communications (PACS) systems practically. Key words: Image compression, segmentation, wavelet decomposition, fuzzy logic , decompression, compression ratio.
Highlights
A new medical image compression technique is required for the reduction in diagnostic analysis cost, storage cost and transmission time without affecting quality of the image
The performance of the proposed algorithm are evaluated in terms of quality measures such as mean square error (MSE), peak to signal to noise ratio (PSNR), CR, Encryption time and Decryption time results for different medical images and is tabulated as follows; The six medical images with different sizes and types are taken for an experiment
The compression ratio is increased by the existing compression schemes only by reducing the quality of the medical image
Summary
A new medical image compression technique is required for the reduction in diagnostic analysis cost, storage cost and transmission time without affecting quality of the image. Liu et al (2002) proposed a new method for chromosome image compression based on an important characteristic of the images. Both Doukas et al (2005), and Doukas and Maglogiannis (2007) presented medical image compression using wavelet transform on mobile devices with ROI coding support. SPIHT is applied for lower energy clusters (Non ROI part of an image) and ROI parts are not compressed by which medical information maintained. Step 6: SPIHT algorithm is used to compress Non ROI parts of an image. S is the standard deviation, and N is the number of data points These features are given as input to fuzzy logic system.
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