Computer aided wireless capsule endoscopy video segmentation

Authors

  • Li Baopu,

    1. School of Medicine, Shenzhen University, Shenzhen 518055, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen 518052, China; and Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518052, China
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  • Xu Guoqing,

    1. Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen 518056, China
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  • Zhou Ran,

    1. Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen 518056, China
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  • Wang Tianfu

    1. School of Medicine, Shenzhen University, Shenzhen 518055, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen 518052, China; and Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518052, China
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Abstract

Purpose:

Wireless capsule endoscopy (WCE) opens a new door for the digestive tract examination and diagnosis. However, the examination of its video data is tedious. This study aims to assist a physician to interpret a WCE video by segmenting it into different anatomic parts in the digestive tract.

Methods:

A two level WCE video segmentation scheme is proposed to locate the boundary between the stomach, small intestine, and large intestine. In the rough level, the authors utilize color feature to draw a dissimilarity curve for a WCE video and obtain an approximate boundary. Meanwhile, training data for the fine level segmentation can be collected automatically between the two approximate boundaries of organs to overcome the difficulty of training data collection in traditional approaches. In the fine level, color histogram in the HSI color space is used to segment the stomach and small intestine. Then, color uniform local binary pattern (CULBP) algorithm is applied for discrimination of the small intestine and large intestine, which includes two patterns, namely, color norm and color angle pattern. The CULBP feature is robust to variation of illumination and discriminative for classification. In order to increase the performance of support vector machine, the authors integrate it with the Adaboost approach. Finally, the authors refine the classification results to segment a WCE video into different parts, that is, the stomach, small intestine, and large intestine.

Results:

The average precision and recall are 91.2% and 90.6% for the stomach/small intestine classification, 89.2% and 88.7% for the small/large intestine discrimination. Paired t-test also demonstrates a significant better performance of the proposed scheme compared to some traditional methods. The average segmentation error is 8 frames for the stomach/small intestine discrimination, and 14 frames for the small/large intestine segmentation.

Conclusions:

The results have demonstrated that the new video segmentation method can accurately locate the boundary between different organ regions in a WCE video. Such a segmentation result may enhance the efficiency of WCE examination.

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