Segmentation of skin strata in reflectance confocal microscopy depth stacks
Date
2015
Authors
Hames, S.C.
Ardigo, M.
Soyer, H.P.
Bradley, A.P.
Prow, T.W.
Editors
Ourselin, S.
Styner, M.A.
Styner, M.A.
Advisors
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Conference paper
Citation
Proceedings of SPIE, 2015 / Ourselin, S., Styner, M.A. (ed./s), vol.9413, iss.94131U, pp.1-6
Statement of Responsibility
Conference Name
Medical imaging 2015: Image processing (24 Feb 2015 - 26 Feb 2015 : Orlando, US)
Abstract
Reflectance confocal microscopy is an emerging tool for imaging human skin, but currently requires expert human assessment. To overcome the need for human experts it is necessary to develop automated tools for automatically assessing reflectance confocal microscopy imagery. This work presents a novel approach to this task, using a bag of visual words approach to represent and classify en-face optical sections from four distinct strata of the skin. A dictionary of representative features is learned from whitened and normalised patches using hierarchical spherical k-means. Each image is then represented by extracting a dense array of patches and encoding each with the most similar element in the dictionary. Linear discriminant analysis is used as a simple linear classifier. The proposed framework was tested on 308 depth stacks from 54 volunteers. Parameters are tuned using 10 fold cross validation on a training sub-set of the data, and final evaluation was performed on a held out test set. The proposed method generated physically plausible profiles of the distinct strata of human skin, and correctly classified 81.4% of sections in the test set
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Copyright 2015 SPIE