ROI and Non-ROI Image Compression Using Optimal Zero Tree Wavelet and Enhanced Convolutional Neural Network for MRI Images

• Published in: SN computer science Vol. 5; no. 1; p. 38
• Main Authors: Bindu, P. V., Jabeena, A.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.01.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Bandwidths; Compression ratio; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data compression; Data Structures and Information Theory; Fractals; Image coding; Image compression; Image enhancement; Image reconstruction; Image segmentation; Information Systems and Communication Service; Magnetic resonance imaging; Medical imaging; Methods; Neural networks; Optimization; Original Research; Pattern Recognition and Graphics; Software Engineering/Programming and Operating Systems; Tomography; Vision; Wavelet transforms; NON-ROI region; Otsu thresholding; ROI region; Zero tree wavelet transform; Image compression; Convolution neural network
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-023-02335-6

Medical imaging systems generate enormous amounts of information that place a heavy burden on storage and transmission. As a result, image data compression is a major research topic in the field of medical imaging. Therefore, in this paper, an efficient image compression technique is proposed. The proposed technique consists of three stages such as segmentation, image compression, and decompression. Initially, the medical images are collected from the internet. Then, the images are segmented into ROI and Non-ROI regions using the Otsu thresholding technique. Then, the ROI regions are compressed using optimal zero tree wavelet (OZTW) transformand Non-ROI regions are compressed using an enhanced convolution neural network (ECNN). The threshold value of the zero tree wavelet (ZTW) transform and weight and bias value of the convolution neural network (CNN)is optimally selected using the sunflower optimization (SFO) algorithm. After the compression process, the reverse process is carried out for the reconstruction process. The performance of the proposed approach is analyzed based on PSNR, Similarity index, compression ratio, and mean square error.