Department of Computer Science, Louisiana State University, Shreveport, LA, USA.
Skin Res Technol. 2009 Nov;15(4):444-50. doi: 10.1111/j.1600-0846.2009.00387.x.
Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Owing to the difficulty and subjectivity of human interpretation, dermoscopy image analysis has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Although numerous methods have been developed for the detection of lesion borders, very few studies were comprehensive in the evaluation of their results.
In this paper, we evaluate five recent border detection methods on a set of 90 dermoscopy images using three sets of dermatologist-drawn borders as the ground truth. In contrast to previous work, we utilize an objective measure, the normalized probabilistic rand index, which takes into account the variations in the ground-truth images.
The results demonstrate that the differences between four of the evaluated border detection methods are in fact smaller than those predicted by the commonly used exclusive-OR measure.
皮肤镜检查是诊断黑色素瘤和其他色素性皮肤病变的主要成像方式之一。由于人类解释的难度和主观性，皮肤镜图像分析已成为一个重要的研究领域。皮肤镜图像分析中最重要的步骤之一是自动检测病变边界。尽管已经开发了许多用于检测病变边界的方法，但很少有研究全面评估其结果。
在本文中，我们使用三组皮肤科医生绘制的边界作为真实边界，在 90 张皮肤镜图像上评估了五种最新的边界检测方法。与之前的工作不同，我们使用了一种客观的度量标准，归一化概率 Rand 指数，它考虑了真实边界图像的变化。
结果表明，评估的四种边界检测方法之间的差异实际上比常用的“异或”度量所预测的要小。
