Medical Image Compression Based on SPIHT-BAT Algorithms

Abstract

The main aim of this study is to decrease the amount of storage as much as possible and the decoded image seen on the monitor should be as close as possible to the original image. The main goal of this study is to design a fully hybrid system for medical image compression. For this purpose, a hybrid techniques were used to enhance the compression performance, decreasing the computational complexity level and raising the CR (Compression Ratio, the proposed system is adopted on these tools: to design a new fully hybrid image compression system to compress a medical image (Brain Tumour disease type). Furthermore, a new reliable algorithm was proposed in order to identify the ROI (Region of Interest) and NROI (Non-Region of Interest) before compression process. In addition, this algorithm has less computational complexity and efficient, also develop new algorithms to compress the ROI and NROI regions. The first region, ROI, is compressed by cascading of SPIHT and BAT algorithms. Meanwhile, the second region (NROI) is compressed by the 2D-DWT algorithm, finally to design a new coding system by mixed the RLE (Run-Length Encoding) and Huffman coding algorithms to improve the CR. The results indicate that the SPIHT-BAT algorithm has increase the compression ratio better than SPIHT. Furthermore, the result of ROI region better than the result of NROI region. While the result of coding when used (RLE- Huffman) algorithm better than the result when used (RLE) alone or Huffman algorithm. The different parameters of compression process indicate that the proposed system is better than that of Traditional systems that described in literature.

A novel compression methodology for medical images using deep learning for high-speed transmission

“…To demonstrate the proposed compression method's superiority, its efficiency is compared with other well-known techniques in light of PSNR, CR and SSS. The previous methods considered for comparison are PSO-based thresholding BWT-MTF [9] tetrolet [10] QFOM [11] optimized RNN [12] and SPIHT-BAT [13] because all the previous models process the MRI images. Ahilan et al [7] used multilevel thresholding of PSO to extract ROI.…”

“…Salih and Kadhim [13] presented a medical compression method based on set partitioning in hierarchical trees-binary array technique (SPIHT-BAT) algorithm. Reddy et al [14] used discrete cosine transform (DCT), singular value decomposition (SVD) and SPIHT.…”