Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA.
Skin Res Technol. 2013 Feb;19(1):e20-6. doi: 10.1111/j.1600-0846.2011.00602.x. Epub 2012 Jan 11.
Basal cell carcinoma (BCC) is the most common cancer in the US. Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCCs. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails.
In this research, we explore a dirt trail detection and analysis algorithm for extracting, measuring, and characterizing dirt trails based on size, distribution, and color in dermoscopic skin lesion images. These dirt trails are then used to automatically discriminate BCC from benign skin lesions.
For an experimental data set of 35 BCC images with dirt trails and 79 benign lesion images, a neural network-based classifier achieved a 0.902 are under a receiver operating characteristic curve using a leave-one-out approach.
Results obtained from this study show that automatic detection of dirt trails in dermoscopic images of BCC is feasible. This is important because of the large number of these skin cancers seen every year and the challenge of discovering these earlier with instrumentation.
基底细胞癌（BCC）是美国最常见的癌症。皮肤科医生使用皮肤镜来帮助早期发现这些癌症，其依据是识别出常与 BCC 相关的皮肤病变结构。一种新的病变结构，称为污垢痕迹，表现为大小不一的深灰色、棕色或黑色点和团块，呈拉长的簇状分布，边界模糊，常呈曲线状轨迹。
在这项研究中，我们探索了一种污垢痕迹检测和分析算法，用于提取、测量和描述基于大小、分布和颜色的皮肤镜下皮肤病变图像中的污垢痕迹。然后，这些污垢痕迹用于自动区分 BCC 和良性皮肤病变。
对于包含 35 个有污垢痕迹的 BCC 图像和 79 个良性病变图像的实验数据集，使用基于神经网络的分类器在留一法（leave-one-out approach）下的受试者工作特征曲线下面积为 0.902。
本研究的结果表明，自动检测皮肤镜下 BCC 的污垢痕迹是可行的。这一点很重要，因为每年都会有大量的这类皮肤癌，而且用仪器发现这些皮肤癌早期病变具有挑战性。
