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Medical image segmentation is a crucial step in computer-aided diagnosis, treatment planning, and biomedical image analysis. This paper presents a segmentation approach based on Weighted Identification Level Set Evolution (WILSE), which effectively integrates local edge features to improve boundary detection and region accuracy. Traditional segmentation methods often struggle with noise, weak boundaries, and intensity inhomogeneity in medical images. The proposed method overcomes these challenges by assigning adaptive weights to local image features, enhancing the sensitivity of the level set function to significant edges while suppressing irrelevant noise. The model evolves iteratively to refine object boundaries and achieve precise delineation of anatomical structures. Experimental results on various medical imaging modalities such as MRI, CT, and ultrasound demonstrate that the WILSE approach provides superior segmentation accuracy, robustness, and computational efficiency compared to conventional edge-based or region-based methods. This technique shows promising potential for clinical applications where high-precision segmentation is essential for diagnosis and quantitative analysis.
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Copyright © 2019, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Nirmal Jyocee, gn.joyce@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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