Automatic frame reduction of Wireless Capsule Endoscopy video

Tsevas, Spyros; Iakovidis, Dimitris K.; Maroulis, Dimitris; Pavlakis, Emmanouil

doi:10.1109/bibe.2008.4696805

Cited by 21 publications

(13 citation statements)

References 21 publications

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“…A method using color distribution 5 was suggested to differentiate stomach, intestine, and colon tissue. In Tsevas et al, 23 a scheme based on clustering and non-negative matrix factorization was put forward to summarize a CE video to reduce the number of images to be reviewed by physicians. Some schemes have also been developed for abnormalities detection in CE images such as bleeding and ulcer.…”

Section: Introductionmentioning

confidence: 99%

Contourlet-Based Features for Computerized Tumor Detection in Capsule Endoscopy Images

Meng

2011

Ann Biomed Eng

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This article presents a computer-aided detection system for capsule endoscopy (CE) images using contourlet-based color textural features to recognize tumors in the digestive tract. As tumor exhibits rich information in color texture, a novel color texture feature based on contourlet transform is proposed to describe characteristics of tumor in CE images. The proposed features are a hybrid of contourlet transform and uniform local binary pattern, yielding detailed and robust color texture features in multi-directions for CE images. Sequential floating forward search approach is further applied to refine the proposed features. With support vector machine for classification, comprehensive experiments on our present data reveal an encouraging accuracy of 93.6% for tumor detection in CE images using the proposed features.

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Section: Introductionmentioning

confidence: 99%

Contourlet-Based Features for Computerized Tumor Detection in Capsule Endoscopy Images

Meng

2011

Ann Biomed Eng

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“…The author in [3] applies an unsupervised methodology based on clustering and non-negative matrix factorization to summarize the WCE video. It achieves significant reduction of the number of frames.…”

Section: Introductionmentioning

confidence: 99%

WCE video abstracting based on novel color and texture features

Zhao

Meng

2011

2011 IEEE International Conference on Robotics and Biomimetics

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Wireless Capsule Endoscopy (WCE) is a novel imaging technique for the investigation of the entire small bowel. It produces over 50,000 images per examination. It is a highly time-consuming and labor tedious task to review such a large amount of images. Recent research has focused on developing computer aided diagnosis (CAD) systems to reduce the heavy burden of physicians. In this paper, we propose a novel strategy to abstract WCE videos based on abnormality instead of topography. Our scheme utilizes multiple novel features and linear discriminant analysis (LDA) to extract the most representative images in each suspected video clip. Experiments are performed on the current data sets to evaluate our method. The results demonstrate that our strategy achieves significant reduction of the number of frames without losing critical information.

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“…We also compare our method with ARPS and FCM-NMF scheme. These schemes were set same parameters as described in the papers [9,11]. These results of which are presented in Tables 6 and 7.…”

Section: Evaluation Of the Reduction Schemementioning

confidence: 99%

“…An image mining approach that applies a computationally intensive clustering scheme (FCM-NMF) to summarizing the WCE video was proposed to reduce the time for the visual inspection [9,10]. The method is time consuming and quite complicated.…”

Section: Introductionmentioning

confidence: 99%

Wireless Capsule Endoscopy Video Reduction Based on Camera Motion Estimation

Liu

Pan

et al. 2012

J Digit Imaging

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Wireless capsule endoscopy (WCE) is a novel technology aiming for investigating the diseases and abnormalities in small intestine. The major drawback of WCE examination is that it takes a long time to examine the whole WCE video. In this paper, we present a new reduction scheme for WCE video to reduce the examination time. To achieve this task, a WCE video motion model is proposed. Under this motion model, the WCE imaging motion is estimated in two stages (the coarse level and the fine level). In the coarse level, the WCE camera motion is estimated with a combination of Bee Algorithm and Mutual Information. In the fine level, the local gastrointestinal tract motion is estimated with SIFT flow.Based on the result of WCE imaging motion estimation, the reduction scheme preserves key images in WCE video with scene changes. From experimental results, we notice that the proposed motion model is suitable for the motion estimation in successive WCE images. Through the comparison with APRS and FCM-NMF scheme, our scheme can produce an acceptable reduction sequence for browsing and examination.

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Automatic frame reduction of Wireless Capsule Endoscopy video

Cited by 21 publications

References 21 publications

Contourlet-Based Features for Computerized Tumor Detection in Capsule Endoscopy Images

Contourlet-Based Features for Computerized Tumor Detection in Capsule Endoscopy Images

WCE video abstracting based on novel color and texture features

Wireless Capsule Endoscopy Video Reduction Based on Camera Motion Estimation

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