A New Algorithm for Skin Lesion Border Detection in Dermoscopy Images

With advances in medical imaging systems, digital dermoscopy has become one of the major imaging modalities in the analysis of skin lesions. Thus, automated segmentation or border detection has a great impact on the subsequent steps of skin cancer computer-aided diagnosis using demoscopy images. Since dermoscopy images suffer from artifacts such as shading and hair, there is a need for automated and robust artifact attenuation removal and lesion border detection.
A method for segmentation of dermoscopy images is proposed based on active contour. To this end, at first, a simple method for hair pixels is restored and a new scheme for shading detection is proposed. Then, particle swarm optimization (PSO) algorithm is applied to select the best coefficients for converting RGB to gray level. The obtained gray level image is then used as input for multi Otsu method which provides initial contour for border detection using active contour. Finally, Chan and Vese active contour is used for final lesion border detection.
The method is tested on a total of 145 dermoscopic images: 79 cases with benign lesion and 75 cases with melanoma lesion. Mean accuracy, sensitivity and specificity were obtained 94%, 78.5% and 99%, respectively.
Results reveal that the proposed method segments the lesion from dermoscopy images accurately.