WCE Abnormality Detection Based on Saliency and Adaptive Locality-Constrained Linear Coding

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 automati...

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering 2017-01, Vol.14 (1), p.149-159
Main Authors: Yixuan Yuan, Baopu Li, Meng, Max Q.-H
Format: Article
Language:English
Subjects:
Adaptive coding bases
Algorithms
Diagnostic tests
Digestive system
Encoding
Endoscopy
Feature extraction
Hemorrhaging
Image coding
Image color analysis
Image reconstruction
Medical imaging
Medical technology
patch saliency
saliency and adaptive locality-constrained linear coding (SALLC) algorithm
Visualization
wireless capsule endoscopy (WCE) image classification
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Summary: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.
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2016.2610579