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Skin lesion segmentation using an improved snake model.

Huiyu Zhou¹, Gerald Schaefer, M Celebi

¹The Institute of Electronics, Communications and Information Technology (ECIT), Queen's University Belfast, United Kingdom.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

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This study introduces a novel mean shift-based gradient vector flow (GVF) snake algorithm for accurate skin lesion border segmentation in dermoscopy images, improving diagnostic cues.

Area of Science:

Dermatology
Medical Image Analysis
Computer Vision

Background:

Accurate skin lesion border identification is crucial for dermoscopy image analysis and diagnosis.
Traditional snake algorithms can suffer from under- or over-segmentation due to energy force compromises.

Purpose of the Study:

To introduce a new segmentation method for improved skin lesion border detection in dermoscopy images.
To address the limitations of standard snake algorithms in medical image segmentation.

Main Methods:

A mean shift-based gradient vector flow (GVF) snake algorithm was developed.
The method integrates a mean shift operation into the GVF cost function to guide segmentation energies.

Main Results:

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The proposed algorithm accurately identifies skin lesion borders in a diverse range of dermoscopy images.

Experimental results demonstrate superior performance compared to standard methods.

Conclusions:

The mean shift-based GVF snake algorithm offers an effective solution for precise skin lesion segmentation.
This technique enhances the extraction of diagnostic cues from dermoscopy images.