Confident texture-based laryngeal tissue classification for early stage diagnosis support

Sara Moccia; Elena De Momi; Marco Guarnaschelli; Matteo Savazzi; Andrea Laborai; Luca Guastini; Giorgio Peretti; Leonardo S. Mattos

doi:10.1117/1.JMI.4.3.034502

29 September 2017 Confident texture-based laryngeal tissue classification for early stage diagnosis support

Sara Moccia, Elena De Momi, Marco Guarnaschelli, Matteo Savazzi, Andrea Laborai, Luca Guastini, Giorgio Peretti, Leonardo S. Mattos

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 3, 034502 (September 2017). https://doi.org/10.1117/1.JMI.4.3.034502

Abstract

Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited. From the endoscopic videos of 33 patients affected by SCC, a well-balanced dataset of 1320 patches, relative to four laryngeal tissue classes, was extracted. With the best performing feature, the achieved median classification recall was 93% [interquartile range (IQR)=6%]. When excluding low-confidence patches, the achieved median recall was increased to 98% (IQR=5%), proving the high reliability of the proposed approach. This research represents an important advancement in the state-of-the-art computer-assisted laryngeal diagnosis, and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis.

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Sara Moccia, Elena De Momi, Marco Guarnaschelli, Matteo Savazzi, Andrea Laborai, Luca Guastini, Giorgio Peretti, and Leonardo S. Mattos "Confident texture-based laryngeal tissue classification for early stage diagnosis support," Journal of Medical Imaging 4(3), 034502 (29 September 2017). https://doi.org/10.1117/1.JMI.4.3.034502

Received: 26 May 2017; Accepted: 12 September 2017; Published: 29 September 2017

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