Biologically inspired skin lesion segmentation using a geodesic active contour technique

Abstract

GAC techniques show promise in attaining the goal of automatic skin lesion segmentation.

Adaptable texture‐based segmentation by variance and intensity for automatic detection of semitranslucent and pink blush areas in basal cell carcinoma

“…The second set of border methods used variants on a basic geodesic of active contour (GAC) method, employing level set techniques , following the techniques presented in Kasmi et al . The basic GAC method, summarized here, uses convolution of the grayscale (intensity) plane of the original lesion image with a 4 × 4 spatial filter.…”

Automatic lesion border selection in dermoscopy images using morphology and color features

“…Seven of the border segmentation algorithms are based on the geodesic active contour (GAC) technique of Kasmi et al, implemented using the level set algorithm . The initial contour is found by Otsu segmentation of a smoothed image .…”

“…Hence, an accurate lesion segmentation algorithm is a critical step in conventional image processing for automated melanoma diagnosis. Numerous research papers have been published describing a variety of lesion segmentation algorithms . Each of those algorithms has its own advantages and disadvantages, each performing well on certain sets of images.…”

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

“…Region based methods such as the modified JSEG [12], region growing [24], modified watershed [25], and statistical region merging [26] group image pixels into clusters and maintain connectivity between cluster pixels. Edge based methods such as zero-crossing of Laplacian-of-Gaussian [27] and geodesic active contour [28] are aimed at detecting discontinuities in image pixel intensity values [29]. Pixel based methods group similar pixels as belonging to a homogenous cluster that corresponds to an object or part of an object [30] and are widely applied because of their inherent simplicity and robustness [31,32].…”

“…The MATLAB median filter, clear border function, and morphological operations of opening and closing are used in this study. The median filter with structuring element of size 11 × 11 is first used to eliminate hairs and smooths against noise because of its capability to reduce bubble intensity and prevent fuzzy edges [16,28]. It is widely used in digital image processing because it preserves edge information under certain conditions while removing oversegmentation.…”