Published Paper Details:

In this paper, we purposed a near-lossless compression method for medical images to maintain the maximum error limit for each pixel this compression method has a better compression ratio than the lossless compression methods. Transmitting high resolutions data through a network leads to large storage space and Bandwidth. We approached the near-lossless compression method to preserve important diagnostic information for medical images. we purposed a threshold-based Resolution Independent Gradient Edge Detector (RIGED) to predict and decorrelate the image pixels for encoding we used block-based Run Length encoding that encodes the Predicted image into actual values and their numbers of occurrence. The actual image data values are separated from their runs and are stored in a vector array. To implement high compression and better quality of recovered images quantizer with an optimum q level is proposed. To remove inter-pixel redundancy the proposed method uses a resolution-independent gradient edge detector (RIGED) after quantization block-based encoding is used for removing coding redundancy. The proposed method is evaluated on volumetric 8-bit and 16-bit standard MR image data-set The performance of the proposed technique showed improvement over the existing techniques SPHIT, JPEG-LS, and JPEG 2000by 40.89%, 34.50%, and 22.36% respectively in terms of Bits per Pixel (BPP).