Detecting abnormalities in endoscopic capsule images using color wavelet features and feed-forward neural networks

By Lima, C.S.; Barbosa, D.; Ramos, J.; Tavares, A.; Carvalho, L.; Monteiro, L&perio

EUROMEDIA 2008 - 14th Annual Scientific Conference on Web Technology, New Media Communications and Telematics Theory Methods, Tools and Applications Medical Imaging and D-TV



This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing 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 encode 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. The proposed approach is supported by a classifier based on multilayer perceptron network 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 87% specificity and 97.4% sensitivity.


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