Saliency-based segmentation of dermoscopic images using color information
This work aims to improve the accuracy of automated early diagnosis of melanoma for medical professionals by providing a more robust skin lesion segmentation method, which is an incremental improvement over existing saliency-based techniques.
This paper addresses the problem of segmenting pigmented skin lesions in dermoscopic images by incorporating color information alongside saliency. The proposed method, which includes a binarization process and new perceptual criteria inspired by human visual perception, achieves accurate skin lesion segmentation and performs satisfactorily compared to existing saliency-based methods on two public databases containing 1497 images.
Skin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided early diagnosis of melanoma. This paper investigates how color information, besides saliency, can be used to determine the pigmented lesion region automatically. Unlike most existing segmentation methods using only the saliency in order to discriminate against the skin lesion from the surrounding regions, we propose a novel method employing a binarization process coupled with new perceptual criteria, inspired by the human visual perception, related to the properties of saliency and color of the input image data distribution. As a means of refining the accuracy of the proposed method, the segmentation step is preceded by a pre-processing aimed at reducing the computation burden, removing artifacts, and improving contrast. We have assessed the method on two public databases, including 1497 dermoscopic images. We have also compared its performance with classical and recent saliency-based methods designed explicitly for dermoscopic images. The qualitative and quantitative evaluation indicates that the proposed method is promising since it produces an accurate skin lesion segmentation and performs satisfactorily compared to other existing saliency-based segmentation methods.