Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes.

TLDR: Dermoscopy images of pigmented lesions are most commonly taken at × 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate to obtain accurate skin lesion segmentation from the background skin.

Abstract: Dermoscopy, otherwise known as skin-surface microscopy or dermatoscopy was originally introduced in 1921 (1) and reintroduced in 1987 (2). One common form of dermoscopy integrates oil immersion under glass with standard × 10 magnifying optics and incident surface lighting permit in vivo visualization of certain features of pigmented melanocytic neoplasms that cannot be observed by visual inspection. Dermoscopy using various rule- or feature-based schemes improves pigmented lesion diagnostic accuracy over the accuracy obtained without this aid for those dermatologists trained in the technique (3–8). Accurate segmentation of the lesion from the background skin is important for computer-assisted diagnostic techniques, both for the determination of lesion shape and lesion color, compared with the color of the surrounding skin (9). In this paper, a method of automatic lesion boundary detection using gradient vector flow (GVF) snakes is presented. Snakes can be defined as curves within an image domain that can move under the influence of internal forces defined within the curve, from features such as smoothness, and external forces computed from the image data (10). Snake algorithms are often sensitive to initialization for effective object segmentation. Initial points for a snake algorithm can be selected by an operator or automatically determined. For this research, an automatic initialization method is used to select an initial set of skin lesion border points for the dermoscopy image data set examined. The remainder of this paper is organized as follows. In the following section, an overview of traditional snake algorithms is presented, along with limitations of traditional snakes in the domain of pigmented images. In the third section, GVF snakes are presented. In the fourth section, the application of GVF snakes for skin lesion border determination in dermoscopy images is given. The automatic initialization method and the two-step operation of GVF snakes are described. In the fifth section, experimental results and discussion are presented comparing the GVF snake algorithm and its comparison with the histogram analysis technique developed by Pagadala (11). Finally, conclusions from this research are provided.