A method for enhancing segmentation of a child skin scope image of a SAM

By employing adaptive illumination correction and skin color standardization, combined with a multi-scale boundary refinement network, the problems of high computational complexity and blurred boundaries in pediatric dermoscopy image segmentation are solved, achieving efficient image segmentation results that meet the needs of real-time clinical applications.

CN122415643APending Publication Date: 2026-07-17WUHAN UNIV OF SCI & TECH +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
WUHAN UNIV OF SCI & TECH
Filing Date
2026-04-15
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing image segmentation methods have high computational complexity when processing children's dermoscopic images and cannot effectively handle the unique circular reflection patterns and blurred boundary areas of children's skin, resulting in insufficient segmentation accuracy.

Method used

By employing adaptive illumination correction and skin color adaptive normalization, circular reflective areas are detected and repaired. Combined with a multi-scale boundary refinement network and bi-branch feature fusion, the segmentation accuracy of blurred boundary areas is improved, while reducing computational complexity.

Benefits of technology

While maintaining segmentation accuracy, the computational complexity is reduced. The inference time for a single image is approximately 50 to 80 milliseconds, meeting the needs of real-time clinical applications and effectively addressing the problem of blurred lesion boundaries in pediatric dermoscopy images.

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Abstract

The present application relates to the technical field of medical image processing, and discloses a kind of enhanced SAM's child dermatoscope image segmentation method, comprising: collecting child dermatoscope image and repairing circular reflection area, through adaptive light correction and skin color standardization processing to enhance lesion visibility and eliminate individual color difference;Image is divided into non-overlapping block sequence, projection and fusion learnable position coding to obtain feature sequence;Through double-branch parallel extraction global semantics and local boundary texture features, using spatial perception gate mechanism to adaptively fuse double-branch features;Double-layer residual connection is carried out to the fusion features and stacked multiple layers to form deep encoder, obtain multi-scale feature pyramid;Layer by layer up-sampling and fusing each layer feature, through multi-scale boundary refinement network to deal with fuzzy boundary to obtain binary segmentation mask.The present application can improve the segmentation accuracy and computational efficiency of fuzzy boundary lesions in child dermatoscope image.
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