Region of interest based selective coding technique for volumetric MR image sequence

Abstract

Advanced image scanning techniques produce high resolution medical images such as CT, MRI which in turn needs large storage space and bandwidth for transmitting over a network. Lossless compression is preferred for medical images to preserve important diagnostic details. However, it is only sufficient to maintain the high quality of an image in a diagnostically important region, namely Region of interest (ROI) for an accurate diagnosis. Non -ROI portion when compressed near-losslessly does not affect the image quality but reduces the file size effectively. We propose a compression technique where the prediction is done by Resolution Independent Gradient Edge Detector (RIGED) to de-correlate the image pixels and block-based arithmetic coding is used for encoding. The optimal threshold value, optimal q-level and the block-based coding removes inter-pixel, psycho-visual and coding redundancy from non-ROI part to achieve high compression whereas ROI part is compressed losslessly by removing inter-pixel and coding redundancy only. In this paper, optimal threshold-based predictive lossless compression in the ROI and optimal quantization (q) based near-lossless compression in the rest of the region is proposed. The proposed method is evaluated on volumetric 8 bit and 16 bit standard MR image data-set and validated on real patient’s 16 bit depth MR images collected from local hospitals. The performance of the proposed technique showed improvement over the existing techniques JPEG 2000, JPEG-LS, M-CALIC, JP3D, and CALIC by 40.89%, 34.50%, 32.92%, 22.36%, and 17.25% respectively in terms of Bits per Pixel (BPP).