Medical images compression for remote diagnosis using modified SPIHT data organization and fidelity enhancement filter

Abstract

In this work, an 8 3 8 Discrete Cosine Transform (DCT) approach is adopted to perform DCT shrinkage, followed by modified set partitioning in hierarchical trees (SPIHT) data organization and fidelity enhancement filter for reducing the memory required to store a remote diagnosis and rapidly transmit it. The DCT shrinkage has the ability to retain the detailed characteristics of an image. By means of a simple transformation to gather the DCT spectrum data with the same frequency domain, the DCT shrinkage exploits all the characteristics of individual blocks to a global framework. In this scheme, insignificant DCT coefficients that correspond to the same spatial location in the high-frequency sub-bands can be used to reduce the redundancy by a combined function proposed in association with the modified SPIHT. Meanwhile, quad-tree decomposition and a set of morphological filters for reducing the artifacts are presented. This set of filters employs 8 predefined morphological operations, namely 4 structuring elements (SE), each of which includes both dilation and erosion operations. The voting strategy is used to select the most suitable morphological filter for each block. Simulation results show that the image compression reduced the computational complexity to only a half of the wavelet based sub-band decomposition and improved the quality of the reconstructed medical image in terms of both the peak signal-to-noise ratio (PSNR) and the perceptual results close to JPEG2000 and the original SPIHT at the same bit rate.

Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries

“…Lossy compression is used to compress the SROI using medium compression rates to ensure all diagnostically relevant information is preserved with sufficient detail. The algorithm can operate either with a constant or a variable bit rate (detailed below) to compress the SROI data [ 27 ].…”