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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