Projection-Based Medical Image Compression for Telemedicine Applications

Abstract

Recent years have seen great development in the field of medical imaging and telemedicine. Despite the developments in storage and communication technologies, compression of medical data remains challenging. This paper proposes an efficient medical image compression method for telemedicine. The proposed method takes advantage of Radon transform whose basis functions are effective in representing the directional information. The periodic re-ordering of the elements of Radon projections requires minimal interpolation and preserves all of the original image pixel intensities. The dimension-reducing property allows the conversion of 2D processing task to a set of simple 1D task independently on each of the projections. The resultant Radon coefficients are then encoded using set partitioning in hierarchical trees (SPIHT) encoder. Experimental results obtained on a set of medical images demonstrate that the proposed method provides competing performance compared with conventional and state-of-the art compression methods in terms of compression ratio, peak signal-to-noise ratio (PSNR), and computational time.

Lossy Image Compression in a Preclinical Multimodal Imaging Study

“…For an increase of the CR to a value of 40:1 (PSNR of 70 dB), only one of the three radiologists demonstrated statistical differences relative to the baseline. More recently, in [ 22 ], a compression method based on the encoding of Radon-transform coefficients was proposed, and tested on abdominal MRI images, as well as on axial CT views of the pancreas. In this study, PSNR values from 39 to 50 dB, and SSIM between 0.72 and 0.98 with associated CR between 5:1 and 114:1 were reported, while still preserving the appearance of the images.…”

A Fast Fractal Based Compression for MRI Images

“…Zhang et al [26] proposed an MRI image classifier based on Particle Swarm Optimization and kernel support vector machine. Juliet et al [27] proposed a projection-based medical image compression algorithm. Discrete radon transform (DRT) was used to effectively represent direction information of the image, and RANHT was used to encode Randon transform coefficients.…”