Multi-Solution Analysis for Medical Image Segmentation Using Wavelet Transforms.

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Rahasa M. Sahu, Rajeev Shrivastava, Shashikant V. Golande, Ravindra R. Bombale, Basappa V. Karlatthe, Shriganesh S. Mane

Abstract

In this research, we report on an experimental investigation that aims to construct an automatic image segmentation system for detecting ROI in medical pictures taken by medical scanners such as MRI. Wavelet transforms have been employed in the suggested segmentation system to perform multiresolution analysis (MRA). The process of classifying malignancies in human organs using shape or Gray-level information from scanner output is especially difficult since soft tissues' Gray-level intensity overlaps and the shape of the organs varies across successive slices in the medical stack. Using three-dimensional wavelet decomposition, coefficients thresholding, and object reconstruction, this study demonstrates the use of wavelet transform to accomplish these tasks.The suggested approach is initially tested on simulated data before being used to process different brain regions and highlight the relevant ones. The work aims to introduce the three-dimensional wavelet transform, explore its application to denoising volume data, and suggest the subsequent data extraction to enable their categorization. In order to provide the optimal algorithmic solution to this issue, the research compares numerical results obtained using several wavelet functions and thresholding techniques with the expertise of an expert.

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