Huang Adam, Kwan Shun-Yuen, Chang Wen-Yu, Liu Min-Yin, Chi Min-Hsiu, Chen Gwo-Shing
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:3315-8. doi: 10.1109/EMBC.2013.6610250.
Artifacts such as hair are major obstacles to automatic segmentation of pigmented skin lesion images for computer-aided diagnosis systems. It is even more challenging to process clinical images taken by a regular digital camera, where the shadows of the skin texture may mimic hair-like curvilinear structures. In this study, we examined the popular DullRazor software with a dataset of 20 clinical images. The software, specifically designed for dermoscopic images, was unable to remove fine hairs or hairs in the shade. Alternatively, we proposed using conventional matched filters to enhance curvilinear structures. The more complicate hair intersection patterns, which were known to generate low matched filtering responses, were recovered by using region growing algorithms from nearby detected hair segments with linear discriminant analysis (LDA) based on a color similarity criterion. The preliminary results indicated the proposed method was able to remove more fine hairs and hairs in the shade, and lower false hair detection rate by 58% (from 0.438 to 0.183) as compared to the DullRazor's approach.
对于计算机辅助诊断系统而言，诸如毛发之类的伪像，是色素沉着性皮肤病变图像自动分割的主要障碍。处理普通数码相机拍摄的临床图像则更具挑战性，因为皮肤纹理的阴影可能会模仿毛发状的曲线结构。在本研究中，我们使用包含20张临床图像的数据集对广为人知的DullRazor软件进行了测试。该软件是专门为皮肤镜图像设计的，无法去除细毛或处于阴影中的毛发。相反，我们提出使用传统的匹配滤波器来增强曲线结构。通过基于颜色相似性准则的线性判别分析（LDA），利用区域生长算法从附近检测到的毛发段中恢复已知会产生低匹配滤波响应的更复杂的毛发交叉模式。初步结果表明，与DullRazor方法相比，所提出的方法能够去除更多的细毛和处于阴影中的毛发，并将误检毛发率降低58%（从0.438降至0.183）。
