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.
翻译:皮肤部位偏移是高效的非侵入性计算机辅助黑素瘤早期诊断的关键步骤之一。本文调查了除了显著性外,如何利用彩色信息自动确定色素损伤区域。与大多数现有的光谱分解方法不同,我们建议采用一种新颖的方法,在人类视觉感知的启发下,结合与输入图像分布的显著性和颜色特性有关的新概念性标准,采用二进制进程,同时采用新的概念性标准。作为改进拟议方法准确性的一种手段,分解步骤之前先进行预处理,目的是减少计算负担,去除文物,改进对比。我们评估了两个公共数据库的方法,包括1497张脱温图像。我们还将其性能与专门为脱温图像设计的古典和最近突出性方法进行了比较。定性和定量评估表明,拟议方法很有希望,因为它产生准确的皮肤分解,并与其他现有突出分解法相比,表现得令人满意。