Share Email Print
cover

Proceedings Paper

Nuclei graph local features for basal cell carcinoma classification in whole slide images
Author(s): David Romo-Bucheli; Germán Corredor; Juan D. García-Arteaga; Viviana Arias; Eduardo Romero
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Evidence based medicine aims to provide a quantifiable framework to support cancer optimal treatment selection. Pathological examination is the main evidence used in medical management, yet the level of quantification is low and highly dependent on the examiner expertise. This paper presents and evaluates a method to extract graph based topological features from skin tissue images to identify cancerous regions associated to basal cell carcinoma. The graph features constitute a quantitative measure of the architectural tissue organization. Results show that graph topological features extracted from a nuclei based distance graph, particularly those related to local density, have a high predictive value in the automated detection of basal cell carcinoma. The method was evaluated using a leave-one-out validation scheme in a set of 9 skin Whole Slide Images obtaining a 0.76 F-score in distinguishing basal cell carcinoma regions in skin tissue whole slide images.

Paper Details

Date Published: 26 January 2017
PDF: 9 pages
Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101600Q (26 January 2017); doi: 10.1117/12.2257386
Show Author Affiliations
David Romo-Bucheli, Univ. Nacional de Colombia (Colombia)
Germán Corredor, Univ. Nacional de Colombia (Colombia)
Juan D. García-Arteaga, Univ. Nacional de Colombia (Colombia)
Viviana Arias, Univ. Nacional de Colombia (Colombia)
Eduardo Romero, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 10160:
12th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Jorge Brieva; Ignacio Larrabide; , Editor(s)

© SPIE. Terms of Use
Back to Top