Saliency-based segmentation of dermoscopic images using color information

TL;DR

This paper presents a novel saliency and color-based segmentation method for dermoscopic images, improving accuracy in skin lesion detection by incorporating perceptual criteria inspired by human vision.

Contribution

It introduces a new segmentation approach combining saliency, color information, and perceptual criteria, with a pre-processing step to enhance performance on dermoscopic images.

Findings

Achieves accurate skin lesion segmentation on public dermoscopic datasets.

Outperforms classical and recent saliency-based segmentation methods.

Demonstrates robustness and efficiency in pre-processing and segmentation accuracy.

Abstract

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.