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Proceedings Paper

Global-constrained hidden Markov model applied on wireless capsule endoscopy video segmentation
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Paper Abstract

Accurate analysis of wireless capsule endoscopy (WCE) videos is vital but tedious. Automatic image analysis can expedite this task. Video segmentation of WCE into the four parts of the gastrointestinal tract is one way to assist a physician. The segmentation approach described in this paper integrates pattern recognition with statiscal analysis. Iniatially, a support vector machine is applied to classify video frames into four classes using a combination of multiple color and texture features as the feature vector. A Poisson cumulative distribution, for which the parameter depends on the length of segments, models a prior knowledge. A priori knowledge together with inter-frame difference serves as the global constraints driven by the underlying observation of each WCE video, which is fitted by Gaussian distribution to constrain the transition probability of hidden Markov model.Experimental results demonstrated effectiveness of the approach.

Paper Details

Date Published: 15 May 2012
PDF: 8 pages
Proc. SPIE 8384, Three-Dimensional Imaging, Visualization, and Display 2012, 83840X (15 May 2012); doi: 10.1117/12.919595
Show Author Affiliations
Yiwen Wan, Univ. of North Texas (United States)
Prakash Duraisamy, Univ. of North Texas (United States)
Mohammad S. Alam, Univ. of South Alabama (United States)
Bill Buckles, Univ. of North Texas (United States)


Published in SPIE Proceedings Vol. 8384:
Three-Dimensional Imaging, Visualization, and Display 2012
Bahram Javidi; Jung-Young Son, Editor(s)

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