Lee Hyunju, Kwon Kiwoon
Department of Mathematics, Dongguk Univesity_Seoul, Seoul, 04620 Republic of Korea.
Biomed Eng Lett. 2019 Dec 7;10(1):171-179. doi: 10.1007/s13534-019-00142-8. eCollection 2020 Feb.
A typical diagnosis of malignant melanoma involves three major steps: segmentation of a lesion from the input color image, feature extraction from the separated lesion, and classification to distinguish malignant from benign melanomas based on features obtained. We suggest new methods for segmentation, feature extraction, and classification compared. We replaced edge-imfill method with U-Otsu method for segmentation, the previous features with new features for the criteria ABCD (asymmetry, border irregularity, color variegation, diameter) criteria, and the median thresholding with weighted receiver operating characteristic thresholding for classification. We used 88 melanoma images and expert's segmentation. All the three steps in the suggested method were compared with the steps in the previous method, with respect to sensitivity, specificity, and accuracy of the 88 samples. For segmentation, the previous and the suggested segmentations were also compared assuming the skin cancer expert's segmentation as a ground truth. All three steps resulted in remarkable improvement in the suggested method.
从输入的彩色图像中分割病变、从分离出的病变中提取特征，以及基于获得的特征进行分类以区分恶性和良性黑色素瘤。我们提出了用于分割、特征提取和分类的新方法并进行了比较。我们用U-Otsu方法取代边缘填充方法进行分割，用基于ABCD（不对称性、边界不规则性、颜色斑驳、直径）标准的新特征取代先前的特征，并用加权接收器操作特征阈值法取代中值阈值法进行分类。我们使用了88张黑色素瘤图像和专家分割结果。就88个样本的敏感性、特异性和准确性而言，将所提方法的所有三个步骤与先前方法的步骤进行了比较。对于分割，还将先前的分割和所提分割与以皮肤癌专家的分割为基准真相的情况进行了比较。所提方法的所有三个步骤均有显著改进。
