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BMC Bioinformatics. 2019 Mar 14;20(Suppl 2):91. doi: 10.1186/s12859-019-2625-8.

Local edge-enhanced active contour for accurate skin lesion border detection.

Author information

1
Bioinformatics, University of Arkansas Little Rock, 2804 S. University, Little Rock, 72204, AR, USA.
2
Computer Science, University of Central Arkansas, 201 Donaghey Avenue, Conway, 72035, AR, USA. skockara@uca.edu.
3
Computer Science, University of Central Arkansas, 201 Donaghey Avenue, Conway, 72035, AR, USA.
4
Computer Science, Texas A&M University-Commerce, 2200 Campbell, Commerce, 75428, TX, USA.
5
Dermatology, University of Arkansas for Medical Sciences, 324 Campus Dr., Little Rock, 72205, AR, USA.
6
Systems Engineering, University of Arkansas for Medical Sciences, 2804 S. University, Little Rock, 72204, AR, USA.

Abstract

BACKGROUND:

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method.

RESULT:

Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133.

CONCLUSION:

We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

KEYWORDS:

Dermoscopy; Skin lesion border detection; Skin lesion segmentation

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