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Proceedings Paper

Automated cellular pathology in noninvasive confocal microscopy
Author(s): Monica Ting; James Krueger; Daniel Gareau
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Paper Abstract

A computer algorithm was developed to automatically identify and count melanocytes and keratinocytes in 3D reflectance confocal microscopy (RCM) images of the skin. Computerized pathology increases our understanding and enables prevention of superficial spreading melanoma (SSM). Machine learning involved looking at the images to measure the size of cells through a 2-D Fourier transform and developing an appropriate mask with the erf() function to model the cells. Implementation involved processing the images to identify cells whose image segments provided the least difference when subtracted from the mask. With further simplification of the algorithm, the program may be directly implemented on the RCM images to indicate the presence of keratinocytes in seconds and to quantify the keratinocytes size in the en face plane as a function of depth. Using this system, the algorithm can identify any irregularities in maturation and differentiation of keratinocytes, thereby signaling the possible presence of cancer.

Paper Details

Date Published: 17 March 2014
PDF: 10 pages
Proc. SPIE 8940, Optical Biopsy XII, 89400A (17 March 2014); doi: 10.1117/12.2040213
Show Author Affiliations
Monica Ting, The Rockefeller Univ. (United States)
High Technology High School (United States)
James Krueger, The Rockefeller Univ. (United States)
Daniel Gareau, The Rockefeller Univ. (United States)


Published in SPIE Proceedings Vol. 8940:
Optical Biopsy XII
Robert R. Alfano; Stavros G. Demos, Editor(s)

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