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2017 | OriginalPaper | Buchkapitel

Automatic Classification of Non-informative Frames in Colonoscopy Videos Using Texture Analysis

verfasst von : Cristian Ballesteros, Maria Trujillo, Claudia Mazo, Deisy Chaves, Jesus Hoyos

Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Verlag: Springer International Publishing

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Abstract

Colonoscopy is the most recommended test for preventing/detecting colorectal cancer. Nowadays, digital videos can be recorded during colonoscopy procedures in order to develop diagnostic support tools. Once video-frames are annotated, machine learning algorithms have been commonly used in the classification of normal-vs-abnormal frames. However, automatic analysis of colonoscopy videos becomes a challenging problem since segments of a video annotated as abnormal, such as cancer or polypos, may contain blurry, sharp and bright frames. In this paper, a method based on texture analysis, using Local Binary Patterns on the frequency domain, is presented. The method aims to automatically classify colonoscopy video frames into either informative or non-informative. The proposed method is evaluated using videos annotated by gastroenterologists for training a support vector machines classifier. Experimental evaluation shown values of accuracy over 97%.

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Titel: Automatic Classification of Non-informative Frames in Colonoscopy Videos Using Texture Analysis
verfasst von: Cristian Ballesteros
Maria Trujillo
Claudia Mazo
Deisy Chaves
Jesus Hoyos
Verlag: Springer International Publishing
Buch: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Print ISBN: 978-3-319-52276-0

Electronic ISBN: 978-3-319-52277-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-52277-7_49

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