Department of Electrical and Electronic Engineering, NICTA Victoria Research Laboratory, Universty of Melbourne, Parkville, Melbourne, Victoria 3010, Australia.
Comput Med Imaging Graph. 2011 Mar;35(2):105-15. doi: 10.1016/j.compmedimag.2010.08.001. Epub 2010 Sep 15.
Automated border detection is one of the most important steps in dermoscopy image analysis. Although numerous border detection methods have been developed, few studies have focused on determining the optimal color channels for border detection in dermoscopy images. This paper proposes an automatic border detection method which determines the optimal color channels and performs hybrid thresholding to detect the lesion borders. The color optimization process is tested on a set of 30 dermoscopy images with four sets of dermatologist-drawn borders used as the ground truth. The hybrid border detection method is tested on a set of 85 dermoscopy images with two sets of ground truth using various metrics including accuracy, precision, sensitivity, specificity, and border error. The proposed method, which is comprised of two stages, is designed to increase specificity in the first stage and sensitivity in the second stage. It is shown to be highly competitive with three state-of-the-art border detection methods and potentially faster, since it mainly involves scalar processing as opposed to vector processing performed in the other methods. Furthermore, it is shown that our method is as good as, and in some cases more effective than a dermatology registrar.
自动边界检测是皮肤镜图像分析中最重要的步骤之一。尽管已经开发了许多边界检测方法，但很少有研究关注确定皮肤镜图像中边界检测的最佳颜色通道。本文提出了一种自动边界检测方法，该方法确定最佳颜色通道并执行混合阈值处理以检测病变边界。颜色优化过程在一组 30 张皮肤镜图像上进行，使用四组皮肤科医生绘制的边界作为地面实况。混合边界检测方法在一组 85 张皮肤镜图像上进行，使用两种地面实况，使用各种指标进行测试，包括准确性、精度、敏感性、特异性和边界误差。所提出的方法由两个阶段组成，旨在第一阶段提高特异性，在第二阶段提高敏感性。与三种最先进的边界检测方法相比，它具有很强的竞争力，并且由于它主要涉及标量处理，而不是其他方法中执行的向量处理，因此速度可能更快。此外，结果表明，我们的方法与皮肤科医师相当，在某些情况下甚至比皮肤科医师更有效。
