Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

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

doi:10.1117/12.2253860

3 March 2017 Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

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

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101340D (2017) https://doi.org/10.1117/12.2253860
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

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Sander Klomp, Fons van der Sommen, Anne-Fré Swager, Svitlana Zinger, Erik J. Schoon, Wouter L. Curvers, Jacques J. Bergman, and Peter H. N. de With "Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340D (3 March 2017); https://doi.org/10.1117/12.2253860

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