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Proceedings Paper

A low dimensional entropy-based descriptor of several tissues in skin cancer histopathology samples
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Paper Abstract

The use of low-level visual features to assign high level labels in datasets of histopathology images is a possible solution to the problems derived from manual labeling by experts. However, in many cases, the visual cues are not enough. In this article we propose the use of features derived exclusively from the spatial distribution of the cell nuclei. These features are calculated using the weight of k-nn graphs constructed from the distances between cells. Results show that there are k values with enhanced discriminatory power, especially when comparing cancerous and non-cancerous tissue.

Paper Details

Date Published: 22 December 2015
PDF: 7 pages
Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 968102 (22 December 2015); doi: 10.1117/12.2211528
Show Author Affiliations
Pablo Álvarez, Univ. Nacional de Colombia (Colombia)
Germán Corredor, Univ. Nacional de Colombia (Colombia)
Juan D. García-Arteaga, Univ. Nacional de Colombia (Colombia)
Eduardo Romero, Univ. Nacional de Colombia (Colombia)

Published in SPIE Proceedings Vol. 9681:
11th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Juan D. García-Arteaga; Jorge Brieva, Editor(s)

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