WCE abnormality detection based on saliency and adaptive locality-constrained linear coding

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

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Original languageEnglish
Article number7589047
Pages (from-to)149-159
Journal / PublicationIEEE Transactions on Automation Science and Engineering
Issue number1
Online published12 Oct 2016
Publication statusPublished - Jan 2017
Externally publishedYes


Wireless capsule endoscopy (WCE) has become a widely used diagnostic technique for the digestive tract, at the price of a large volume of data that needs to be analyzed. To tackle this problem, a new computer-aided system using novel features is proposed in this paper to classify WCE images automatically. In the feature learning stage, to obtain the representative visual words, we first calculate the color scale invariant feature transform from the bleeding, polyp, ulcer, and normal WCE image samples separately and then apply $K$ -means clustering on these features to obtain visual words. These four types of visual words are combined together to composite the representative visual words for classifying the WCE images. In the feature coding stage, we propose a novel saliency and adaptive locality-constrained linear coding (SALLC) algorithm to encode the images. The SALLC encodes patch features based on adaptive coding bases, which are calculated by the distance differences among the features and the visual words. Moreover, it imposes the patch saliency constraint on the feature coding process to emphasize the important information in the images. The experimental results exhibit a promising overall recognition accuracy of 88.61%, validating the effectiveness of the proposed method.

Research Area(s)

  • Adaptive coding bases, Patch saliency, Saliency and adaptive locality-constrained linear coding (SALLC) algorithm, Wireless capsule endoscopy (WCE) image classification