Abstract: The image compression techniques are the fast-growing methods and have developed on large scale. Among them, wavelet-based compression methods are most promising and efficient techniques widely used in the field of medical image processing and transmission. The compression techniques are treated as lossy or lossless models and these can be applied on the medical images considering different situations. The medical image parts are separated into two regions. The central part of the image is treated as core region called region of interest (ROI) and others are treated as non-ROI. ROI based coding techniques are considered as most important in the medical field for efficient transmission of clinical data. The proposed method focuses on these concepts. The ROI parts considered are either smooth or textured regions. These are extracted using a segmentation method called singular value decomposition (SVD) method. An efficient run length coding method called wavelet difference reduction method (WDR) with region growing approach is used to code the extracted ROI part after applying 5/3 based integer wavelet transform. The remaining parts called non-ROI part or background artifacts are coded using Convolution Neural etwork (CNN) method. The proposed method is also restructured as layered structure to achieve adaptive scalable property and named as scalable WDR-CNN (SWDR-CNN) method. The proposed SWDR-CNN method has been evaluated using rate distortion metrics such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The coding gains in terms of PSNR values of SWDR-CNN method has been analysed and compared to popular scalable algorithm like S-SPIHT. The SWDR-CNN method has achieved better coding gain from 0.2 dB to 6 dB in terms of PSNR values. Hence, it is proved that proposed model can be used to code the ROI of images and has applications in the field of medical image data coding and transmission.

Bindulal T.S, “Compression Analysis of Hybrid Model Based on Scalable WDR Method and CNN for ROI-based Medical Image Transmission” International Journal of Advanced Computer Science and Applications(IJACSA), 14(9), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140914

@article{T.S2023,
title = {Compression Analysis of Hybrid Model Based on Scalable WDR Method and CNN for ROI-based Medical Image Transmission},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140914},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140914},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {9},
author = {Bindulal T.S}
}