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Proceedings Paper

Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging
Author(s): Sander Klomp; Fons van der Sommen; Anne-Fré Swager; Svitlana Zinger; Erik J. Schoon; Wouter L. Curvers; Jacques J. Bergman; Peter H. N. de With
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Paper Abstract

Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett’s Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance.

Paper Details

Date Published: 3 March 2017
PDF: 10 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340D (3 March 2017); doi: 10.1117/12.2253860
Show Author Affiliations
Sander Klomp, Technische Univ. Eindhoven (Netherlands)
Fons van der Sommen, Technische Univ. Eindhoven (Netherlands)
Anne-Fré Swager, Academisch Medisch Ctr. (Netherlands)
Svitlana Zinger, Technische Univ. Eindhoven (Netherlands)
Erik J. Schoon, Catharina Hospital (Netherlands)
Wouter L. Curvers, Catharina Hospital (Netherlands)
Academisch Medisch Ctr. (Netherlands)
Jacques J. Bergman, Academisch Medisch Ctr. (Netherlands)
Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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