Automated basal cell carcinoma detection in high-definition optical coherence tomography

Automated basal cell carcinoma detection in high-definition optical coherence tomography
Title:
Automated basal cell carcinoma detection in high-definition optical coherence tomography
Other Titles:
2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
DOI:
10.1109/EMBC.2016.7591332
Keywords:
Publication Date:
16 August 2016
Citation:
A. Li et al., "Automated basal cell carcinoma detection in high-definition optical coherence tomography," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, 2016, pp. 2885-2888. doi: 10.1109/EMBC.2016.7591332
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.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1558-4615
ISBN:
978-1-4577-0220-4
978-1-4577-0219-8
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