Improved Bag of Feature for Automatic Polyp Detection in Wireless Capsule Endoscopy Images

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

90 Scopus Citations
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Original languageEnglish
Article number7052426
Pages (from-to)529-535
Journal / PublicationIEEE Transactions on Automation Science and Engineering
Issue number2
Online published2 Mar 2015
Publication statusPublished - Apr 2016
Externally publishedYes


Wireless capsule endoscopy (WCE) needs computerized method to reduce the review time for its large image data. In this paper, we propose an improved bag of feature (BoF) method to assist classification of polyps in WCE images. Instead of utilizing a single scale-invariant feature transform (SIFT) feature in the traditional BoF method, we extract different textural features from the neighborhoods of the key points and integrate them together as synthetic descriptors to carry out classification tasks. Specifically, we study influence of the number of visual words, the patch size and different classification methods in terms of classification performance. Comprehensive experimental results reveal that the best classification performance is obtained with the integrated feature strategy using the SIFT and the complete local binary pattern (CLBP) feature, the visual words with a length of 120, the patch size of 8∗8, and the support vector machine (SVM). The achieved classification accuracy reaches 93.2%, confirming that the proposed scheme is promising for classification of polyps in WCE images.

Research Area(s)

  • Improved bag of feature method, integration of features, polyp detection, wireless capsule endoscopy images