Supervised 3D Graph-based Automated Epidermal Thickness Estimation

Supervised 3D Graph-based Automated Epidermal Thickness Estimation
Title:
Supervised 3D Graph-based Automated Epidermal Thickness Estimation
Other Titles:
2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP)
Publication Date:
04 August 2017
Citation:
Srivastava, R., Yow, A. P., Cheng, J., Wong, D. W., & Tey, H. L. (2017, August). Supervised 3D graph-based automated epidermal thickness estimation. In Signal and Image Processing (ICSIP), 2017 IEEE 2nd International Conference on (pp. 297-301).
Abstract:
Skin biopsies are frequently performed for the diagnosis of skin diseases. However, the invasive nature of biopsies confers many disadvantages. Non-invasive imaging using optical coherence tomography (OCT) with automated disease detection can help reduce requirements for biopsies. One of the features in skin diseases is abnormal epidermal thickness. Automated determination of epidermal thickness requires segmenting the epidermis and this paper presents a novel method of supervised graph-based epidermis segmentation. The cost function for the segmentation method is learned from manually marked images. Epidermis segmentation is followed by epidermal thickness estimation. Evaluation of the method on a dataset containing 10 OCT volumes gives promising results which demonstrate the utility of the proposed method for epidermis segmentation and epidermal thickness measurement.
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Description:
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
ISBN:
978-1-5386-0969-9
978-1-5386-0967-5
978-1-5386-0970-5
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