Nuclei graph local features for basal cell carcinoma classification in whole slide images

David Romo-Bucheli; Germán Corredor; Juan D. García-Arteaga; Viviana Arias; Eduardo Romero

doi:10.1117/12.2257386

26 January 2017 Nuclei graph local features for basal cell carcinoma classification in whole slide images

David Romo-Bucheli; Germán Corredor; Juan D. García-Arteaga; Viviana Arias; Eduardo Romero

Author Affiliations +

Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 101600Q (2017) https://doi.org/10.1117/12.2257386
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina

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.

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David Romo-Bucheli, David Romo-Bucheli, Germán Corredor, Germán Corredor, Juan D. García-Arteaga, Juan D. García-Arteaga, Viviana Arias, Viviana Arias, Eduardo Romero, Eduardo Romero, } "Nuclei graph local features for basal cell carcinoma classification in whole slide images", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101600Q (26 January 2017); doi: 10.1117/12.2257386; https://doi.org/10.1117/12.2257386

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