Sampling with level set for pigmented skin lesion segmentation

Abstract

Melanoma is the deadliest form of skin cancer, and its incidence is increasing. The first step in automated melanoma analysis of dermoscopy images is to segment the area of the lesion from the surrounding skin. To improve the accuracy and adaptability of segmentation, an algorithm called sampling with level set by integrating color and texture (SLS-CT) is proposed that not only designs a new way to incorporate textural and color features in the definition of the energy functional but also utilizes an index called texture level, proposed in this work, to automatically decide the weight of each feature in the combined energies. First, at the preprocessing stage, hair and black frame removal is applied, and a potential lesion area is then obtained using Otsu thresholding and entropy maximization. Thereafter, the probability distribution of prior color in this potential lesion area is calculated as well. Second, Gabor wavelet-based texture features are extracted and integrated with the prior color into the evolving energies of the level set based on the texture level. To achieve global optimization, a Markov chain Monte Carlo sampling approach guided by the combined energy is adopted in evolving the level set, which ultimately defines a border in the image to segment a lesion from normal skin. Finally, morphological operations are used for postprocessing. The experimental results of the proposed algorithm are compared with those of other state-of-the-art algorithms, demonstrating that the proposed algorithm outperforms the other tested ones in terms of accuracy and adaptability to different databases.