Novel 2-D Histogram-Based Soft Thresholding for Brain Tumor Detection and Image Compression

The objective of image compression is to extract meaningful clusters in a given image. Significant groups are possible with absolute threshold values. 1-D histogram-based multilevel thresholding is computationally complex, and reconstructed image visual quality is comparatively low because of equal distribution of energy over the entire histogram plan. So, 2-D histogram-based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel hybrid genetic algorithm, particle swarm optimization, and symbiotic organisms search (hGAPSO-SOS), and the obtained results are compared with state-of-the-art optimization techniques. Recent study reveals that PSNR fails in measuring the visual quality because of mismatch with the objective mean opinion scores (MOS). So, the authors incorporate a weighted PSNR (WPSNR) and visual PSNR (VPSNR). Experimental results examined on magnetic resonance images of brain and results with 2-D histogram reveal that hGAPSO-SOS method can be efficiently and accurately used in multilevel thresholding problem.