Paper Info
Title
Removal Of Non-Informative Frames For Wireless Capsule Endoscopy Video Segmentation
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.
Year
DOI
Venue
2012
10.1109/ICAL.2012.6308214
2012 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS (ICAL)
Keywords
Field
DocType
non-informative frames removal, video segmentation, wireless capsule endoscopy
Histogram,Computer vision,Pattern recognition,Segmentation,Computer science,Discrete cosine transform,Local binary patterns,Image segmentation,Feature extraction,Preprocessor,Artificial intelligence,Contextual image classification
Conference
Volume
Issue
ISSN
null
null
2161-8151
Citations 
PageRank 
References 
3
0.43
8
Authors
5
Name
Order
Citations
PageRank
Zhe Sun150.83
Baopu Li234830.88
Ran Zhou330.43
Huimin Zheng462.63
Max Q.-H. Meng51477202.72