Srivastava Ruchir, Wong Damon Wing Kee
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2885-2888. doi: 10.1109/EMBC.2016.7591332.
Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer. Conventional diagnosis of BCC requires invasive biopsies. Recently, a high-definition optical coherence tomography (HD-OCT) technique has been developed, which provides a non-invasive in vivo imaging method of skin. Good agreements of BCC features between HD-OCT images and histopathological architecture have been found. Therefore it is possible to automatically detect BCC using HD-OCT. This paper presents a novel BCC detection method that consists of four steps: graph based skin surface segmentation, surface flattening, deep feature extraction and the BCC classification. The effectiveness of the proposed method is well demonstrated on a dataset of 5,040 images. It can therefore serve as an automatic tool for screening BCC.
基底细胞癌（BCC）是最常见的非黑色素瘤皮肤癌。传统的基底细胞癌诊断需要进行侵入性活检。最近，一种高清光学相干断层扫描（HD-OCT）技术已经被开发出来，它提供了一种非侵入性的皮肤体内成像方法。已发现HD-OCT图像中的基底细胞癌特征与组织病理学结构之间具有良好的一致性。因此，利用HD-OCT自动检测基底细胞癌是可行的。本文提出了一种新颖的基底细胞癌检测方法，该方法包括四个步骤：基于图的皮肤表面分割、表面扁平化、深度特征提取和基底细胞癌分类。在一个包含5040张图像的数据集上充分证明了所提方法的有效性。因此，它可以作为一种自动筛查基底细胞癌的工具。
