Subband Thresholding for Near-Lossless Medical Image Compression

Abstract

Medical images play a prominent role in diagnostic and treatment planning in the medical arena. Medical images are acquired using several medical imaging modalities like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultra Sound (US), Positron Emission Tomography (PET), and X-Ray. These are used as an image acquisition tool to capture the medical images. Numerous volumes of medical data are produced for processing, storing and transmission over the network for telemedicine services every day. It is essential for medical image applications to reduce storage and to solve transmission problems. Hence, medical image compression techniques are important to reduce the size of the image without any degradation in quality and loss of information in the image because every medical image holds significant data. In this paper, we have proposed a near-lossless medical image compression technique that thresholds the sub-bands to increases the number of zero coefficients. And as an entropy encoder run-length encoding is used to compress the medical image to retain the diagnostic information with high compression efficiency. Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), and Bits Per Pixel (BPP) were used as assessment parameters to examine the performance of our proposed method.