TY - EJOUR
AU - Besharat, Aynaz
AU - Fatemizadeh, Emad
PY - 2024
DA - February
TI - Using Genetic Algorithm for Optimization of Mammograms Image Compression
T2 - Majlesi Journal of Electrical Engineering
VL - 4
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/using-genetic-algorithm-for-optimization-of-mammograms-image-compression/
DO - 10.1234/mjee.v4i3.222
N2 - In this study we created an optimized Region Of Interest (ROI) based JPEG2000 image compression algorithm for mammograms compression. The first step was to perform the standard JPEG2000 algorithm. The second step was to optimize this algorithm in different aspects which are, the type of wavelet transform, the number of decomposition levels of this transform and the quantization table for mammograms compression. Also we tried not to damage the diagnostic information in the images and keep the Peak Signal to Noise Ratio value, high. We achieved high compression ratios up to 165:1 with PSNR=47.96dB which was significantly higher than the previous results studied. At the next step we modified the optimized image compression algorithm in order to compress the mammograms with one square-shaped ROI in a way that we could compress the ROI losslessly. Therefore we could obtain a high total compression ratio and meanwhile preserve the significant medical diagnostic information. In previous studies on ROI-based 8bpp mammograms compression, the highest total CR for the ROI size of 5% and 15% of the entire image, with lossless ROI compression, were 32:1 and 12:1 respectively these values have been raised up to 49.9:1 and 21.33:1 in this study.
IS - 3
PB - OICC Press
KW - Optimization. Genetic Algorithm, Ms. in biomedical engineering, Mammograms Compression, JPEG2000, Region of interest 
EN -