title: Automated basal cell carcinoma detection in high-definition optical coherence tomography.
abstract: 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.
authors.lastname: Annan Li
authors.lastname: Jun Cheng
authors.lastname: Ai Ping Yow
authors.forename: Ruchir
authors.initials: R
authors.lastname: Srivastava
authors.forename: Damon Wing Kee
authors.initials: DW
authors.lastname: Wong
authors.lastname: Hong Liang Tey
authors.lastname: Jiang Liu
mesh_terms: D057205:Automation, Laboratory / Q000379:methods*; D002280:Basal Cell Carcinoma / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D012878:Skin Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000473:pathology; D041623:Tomography, Optical Coherence / Q000379:methods*