Automatic classification of endoscopic images for premalignant conditions of the esophagus

Davide Boschetto; Gloria Gambaretto; Enrico Grisan

doi:10.1117/12.2216826

29 March 2016 Automatic classification of endoscopic images for premalignant conditions of the esophagus

Davide Boschetto, Gloria Gambaretto, Enrico Grisan

Proceedings Volume 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; 978808 (2016) https://doi.org/10.1117/12.2216826
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Barrett's esophagus (BE) is a precancerous complication of gastroesophageal reflux disease in which normal stratified squamous epithelium lining the esophagus is replaced by intestinal metaplastic columnar epithelium. Repeated endoscopies and multiple biopsies are often necessary to establish the presence of intestinal metaplasia. Narrow Band Imaging (NBI) is an imaging technique commonly used with endoscopies that enhances the contrast of vascular pattern on the mucosa. We present a computer-based method for the automatic normal/metaplastic classification of endoscopic NBI images. Superpixel segmentation is used to identify and cluster pixels belonging to uniform regions. From each uniform clustered region of pixels, eight features maximizing differences among normal and metaplastic epithelium are extracted for the classification step. For each superpixel, the three mean intensities of each color channel are firstly selected as features. Three added features are the mean intensities for each superpixel after separately applying to the red-channel image three different morphological filters (top-hat filtering, entropy filtering and range filtering). The last two features require the computation of the Grey-Level Co-Occurrence Matrix (GLCM), and are reflective of the contrast and the homogeneity of each superpixel. The classification step is performed using an ensemble of 50 classification trees, with a 10-fold cross-validation scheme by training the classifier at each step on a random 70% of the images and testing on the remaining 30% of the dataset. Sensitivity and Specificity are respectively of 79.2% and 87.3%, with an overall accuracy of 83.9%.

Citation Download Citation

Davide Boschetto, Gloria Gambaretto, and Enrico Grisan "Automatic classification of endoscopic images for premalignant conditions of the esophagus", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978808 (29 March 2016); https://doi.org/10.1117/12.2216826

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