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

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• 关键词：Colonoscopy videos ; Discrete Fourier Transform ; Local Binary Pattern ; Support Vector Machines
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10125
• 期：1
• 页码：401-408
• 丛书名：Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
• ISBN：978-3-319-52277-7
• 卷排序：10125

文摘

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%.