WCE Abnormality Detection Based on Saliency and Adaptive Locality-Constrained Linear Coding
Publication in refereed journal

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其它資訊
摘要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.
著者Yuan YX, Li BP, Meng MQH
期刊名稱IEEE Transactions on Automation Science and Engineering
出版年份2017
月份1
卷號14
期次1
出版社IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
頁次149 - 159
國際標準期刊號1545-5955
電子國際標準期刊號1558-3783
語言英式英語
關鍵詞Adaptive coding bases,patch saliency,saliency and adaptive locality-constrained linear coding (SALLC) algorithm,wireless capsule endoscopy (WCE) image classification
Web of Science 學科類別Automation & Control Systems;Automation & Control Systems

上次更新時間 2020-25-10 於 03:03