IEEE J Biomed Health Inform. 2019 Mar;23(2):560-569. doi: 10.1109/JBHI.2018.2823499. Epub 2018 Apr 5.
Color has great diagnostic significance in dermatoscopy. Several diagnosis methods are based on the colors detected within a lesion. Malignant lesions frequently show more than three colors, whereas in benign lesions, three or fewer colors are usually observed. Black, red, white, and blue-gray are found more frequently in melanomas than in benign nevi. In this paper, a method to automatically identify the colors of a lesion is presented. A color label identification problem is proposed and solved by maximizing the posterior probability of a pixel to belong to a label, given its color value and the neighborhood color values. The main contribution of this paper is the estimation of the different terms involved in the computation of this probability. Two evaluations are performed on a database of 200 dermoscopic images. The first one evaluates if all the colors detected in a lesion are indeed present in it. The second analyzes if each pixel within a lesion is assigned the correct color label. The results show that the proposed method performs correctly and outperforms other methods, with an average F-measure of 0.89, an accuracy of 0.90, and a Spearman correlation of 0.831.
颜色在皮肤镜检查中有重要的诊断意义。有几种诊断方法是基于病变内检测到的颜色。恶性病变通常显示三种以上的颜色，而良性病变通常观察到三种或更少的颜色。在黑色素瘤中，比在良性痣中更常发现黑色、红色、白色和蓝灰色。在本文中，提出了一种自动识别病变颜色的方法。通过最大化给定颜色值和邻域颜色值的像素属于标签的后验概率，提出并解决了一个颜色标签识别问题。本文的主要贡献是估计了计算这个概率所涉及的不同项的估计。在一个包含 200 张皮肤镜图像的数据库上进行了两次评估。第一次评估病变中检测到的所有颜色是否确实存在于病变中。第二次分析病变内的每个像素是否被分配了正确的颜色标签。结果表明，所提出的方法表现正确，优于其他方法，平均 F 度量为 0.89，准确率为 0.90，Spearman 相关系数为 0.831。
