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

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 p...

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Bibliographic Details
Published in:SN computer science 2024-01, Vol.5 (1), p.38, Article 38
Main Authors: Bindu, P. V., Jabeena, A.
Format: Article
Language:English
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
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Summary: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.
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-02335-6