Lossless compression methods for magnetic resonance imaging using wavelet transform a systematic review
Métodos de compresión sin pérdidas de imágenes de resonancia magnética utilizando transformada wavelet: revisión sistemática
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In medicine, the information from diagnostic images is vital and essential, for this reason, it’s necessary to process them without error margins that could interfere with their reading and analysis. In general terms: images present redundancy between pixels causing them occupy a considerable size ranging from Megabytes (MB) to Gigabytes (GB); the process of transmit them through the network is difficult in terms of storage and computational cost, therefore lossless compression processes must be applied to reduce bandwidth, improve storage capacity and increase transmission speed without affecting the quality of the diagnostic image.
The proposal of this article is based on a systematic review that synthesizes and exposes the characteristics, advantages and disadvantages of extraction techniques of the regions of interest (ROI), the hybrid algorithms of lossless compression of MRI (Magnetic Resonance Imaging) images and, finally, Wavelet transform and the applications proposed, in the future, by the researchers of the reviewed articles are taken as a reference; among the techniques employed, the following are distinguished: EWT (Empirical Wavelet Transform), EZW (Embedded Zerotree of Wavelet), SPIHT (Set partitioning in Hierarchical Trees), and the hybrid-derivative algorithm such as: EWISTARS (Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift). Finally, the selection and automatic extraction of a ROI is carried out by level segmentation and morphological operations, such as the opening operation. To evaluate the quality of these techniques, the performance metrics MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and CR (Compression Ratio) are described. The results of this research will be useful for researchers, who are starting their incursion into the area, to extend their vision of medical image processing.
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Accessed: Nov 15,2020. Available: https://www.nibib.nih.gov/espanol/temas-cientificos/imagen-por-resonancia- magn%C3%A9tica-irm
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