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29 April 2019 Three-dimensional conditional random field for the dermal–epidermal junction segmentation
Julie Robic, Benjamin Perret, Alex Nkengne, Michel Couprie, Hugues Talbot
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Abstract
The segmentation of the dermal–epidermal junction (DEJ) in in vivo confocal images represents a challenging task due to uncertainty in visual labeling and complex dependencies between skin layers. We propose a method to segment the DEJ surface, which combines random forest classification with spatial regularization based on a three-dimensional conditional random field (CRF) to improve the classification robustness. The CRF regularization introduces spatial constraints consistent with skin anatomy and its biological behavior. We propose to specify the interaction potentials between pixels according to their depth and their relative position to each other to model skin biological properties. The proposed approach adds regularity to the classification by prohibiting inconsistent transitions between skin layers. As a result, it improves the sensitivity and specificity of the classification results.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2019/$25.00 © 2019 SPIE
Julie Robic, Benjamin Perret, Alex Nkengne, Michel Couprie, and Hugues Talbot "Three-dimensional conditional random field for the dermal–epidermal junction segmentation," Journal of Medical Imaging 6(2), 024003 (29 April 2019). https://doi.org/10.1117/1.JMI.6.2.024003
Received: 12 November 2018; Accepted: 4 April 2019; Published: 29 April 2019
JOURNAL ARTICLE
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Cited by 6 scholarly publications.
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KEYWORDS
Skin

Image segmentation

3D modeling

Confocal microscopy

3D image processing

Visualization

Image classification

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