Abstract
BackgroundDermoscopy 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.ResultOur 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.ConclusionWe 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.
Highlights
BackgroundImage segmentation is a process of finding meaningful regions in an image. Many of the image processing and analysis methods rely on the accuracy of a proper image segmentation method
Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions
Since melanoma develops in melanocytes, which are special cells on epidermis, it can be detected by visual inspection of skin
Summary
Image segmentation is a process of finding meaningful regions in an image. Many of the image processing and analysis methods rely on the accuracy of a proper image segmentation method. For the images with weakly formed object boundaries (e.g., skin lesions with fuzzy borders), the edge-stop function (ESF) fails to cease the curve move and as a result contour leaks through the object border [18] They suffer in skin lesion segmentation when morphological and color variations exist. It is proven that probability maps incorporated with SPH kernels are robust edge indicator functions that eliminate unwanted leakage problems [18] encountered in active contours This novel SPH based robust edge indicator function is solved using Level Sets [14], which in turn generates accurate skin lesion border detection even for lesions with fuzzy borders
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