Three-dimensional conditional random field for the dermal–epidermal junction segmentation

Julie Robic; Benjamin Perret; Alex Nkengne; Michel Couprie; Hugues Talbot

doi:10.1117/1.JMI.6.2.024003

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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

Author Affiliations +

J. of Medical Imaging, 6(2), 024003 (2019). https://doi.org/10.1117/1.JMI.6.2.024003

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.

Citation Download Citation

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

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