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

Sander R. Klomp, F. van der Sommen, A.-F. Swager, S. Zinger, E.J. Schoon, W.L. Curvers, J.J. Bergman, P.H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)
1 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationMedical Imaging 2017 : Computer-Aided Diagnosis, February 11-16, 2017, Orlando, Florida, USA
EditorsS.G. Armato, N.A. Petrick
PublisherSPIE
Number of pages10
ISBN (Electronic)9781510607132
ISBN (Print)978-1-5106-0713-2
DOIs
Publication statusPublished - 2017
Event2017 SPIE Medical Imaging: Image Processing - Renaissance Orlando at Sea World/Orlando, Orlando, United States
Duration: 11 Feb 201716 Feb 2017

Publication series

NameProceedings of SPIE
Volume10134

Conference

Conference2017 SPIE Medical Imaging
CountryUnited States
CityOrlando
Period11/02/1716/02/17

Keywords

  • Barrett's Esophagus
  • Computer-Aided Diagnosis
  • Endoscopy
  • Esophageal cancer
  • Feature
  • VLE

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