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Classification of endoscopic capsule images by using color wavelet features, higher order statistics and radial basis functions

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6 Author(s)
C. S. Lima ; University of Minho, Industrial Electronics Department, Campus de Azurém 4800-058 Gimarães, Portugal ; D. Barbosa ; J. Ramos ; A. Tavares
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This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing capsule endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to code textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. Third and forth order moments are added to cope with distributions that tend to become non-Gaussian, especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 95% specificity and 93% sensitivity.

Published in:

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

20-25 Aug. 2008