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Title:
removal of non-informative frames for wireless capsule endoscopy video segmentation
Author: Sun Zhe ; Li Baopu ; Zhou Ran ; Zheng Huimin ; Meng Max Q.-H
Source: IEEE International Conference on Automation and Logistics, ICAL
Conference Name: 2012 IEEE International Conference on Automation and Logistics, ICAL 2012
Conference Date: August 15, 2012 - August 17, 2012
Issued Date: 2012
Conference Place: Zhengzhou, China
Keyword: Body fluids ; Discrete cosine transforms ; Endoscopy ; Removal
Indexed Type: EI
ISSN: 2161-8151
ISBN: 9781467303620
Department: (1) School of Software Engineering University of Science and Technology of China Hefei China; (2) Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China; (3) Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Hong Kong
Abstract: Wireless capsule endoscopy (WCE) video segmentation plays an important part in WCE automatic diagnosis since it provides an effective method to help physicians and save time. In the automatic WCE video segmentation process, impurities frames with opaque digestive juice, food residues and excrement not only waste plentiful time, but also cause a lower accuracy of segmentation for its variation of color and pattern. The major impurities which have great affection for WCE video segmentation can be divided into two categories, gastric juice and bubbles. Thus, in this paper, a novel two-stage preprocessing approach is proposed to remove impurities frames in WCE videos. In the first stage, frames of gastric juice are eliminated by using local HS histogram features. In the second stage, a new approach is carried out to remove the bubbles frames in the WCE video, which combines Color Local Binary Patterns (CLBP) algorithm with Discrete Cosine Transform (DCT). K-Nearest Neighbor (KNN) classifier is used in both stages for its rapidity. Experiments demonstrate that the proposed scheme is an effective approach for removing non-informative frames in WCE video and the accuracies of each stage can reach as high as 99.31% and 97.54% respectively. © 2012 IEEE.
English Abstract: Wireless capsule endoscopy (WCE) video segmentation plays an important part in WCE automatic diagnosis since it provides an effective method to help physicians and save time. In the automatic WCE video segmentation process, impurities frames with opaque digestive juice, food residues and excrement not only waste plentiful time, but also cause a lower accuracy of segmentation for its variation of color and pattern. The major impurities which have great affection for WCE video segmentation can be divided into two categories, gastric juice and bubbles. Thus, in this paper, a novel two-stage preprocessing approach is proposed to remove impurities frames in WCE videos. In the first stage, frames of gastric juice are eliminated by using local HS histogram features. In the second stage, a new approach is carried out to remove the bubbles frames in the WCE video, which combines Color Local Binary Patterns (CLBP) algorithm with Discrete Cosine Transform (DCT). K-Nearest Neighbor (KNN) classifier is used in both stages for its rapidity. Experiments demonstrate that the proposed scheme is an effective approach for removing non-informative frames in WCE video and the accuracies of each stage can reach as high as 99.31% and 97.54% respectively. © 2012 IEEE.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15841
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Sun Zhe,Li Baopu,Zhou Ran,et al. removal of non-informative frames for wireless capsule endoscopy video segmentation[C]. 见:2012 IEEE International Conference on Automation and Logistics, ICAL 2012. Zhengzhou, China. August 15, 2012 - August 17, 2012.
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