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Detection of lesions in endoscopic video using textural descriptors on wavelet domain supported by artificial neural network architectures

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4 Author(s)
Karkanis, S.A. ; Dept. of Inf., Athens Univ., Greece ; Iakovidis, D.K. ; Karras, D.A. ; Maroulis, D.E.

Video processing for classification applications in medical imaging is an area with great importance. In this paper a framework for classification of suspicious lesions using the video produced during an endoscopic session is presented. The proposed approach is based on a feature extraction scheme that uses second order statistical information of the wavelet transformation. These features are used as input to a multilayer feedforward neural network (MFNN) architecture, which has been trained using features of normal and tumor regions. The system uses a limited number of frames with a rather small population of training vectors. The classification results are promising, since the system has been proven to be capable to classify and locate regions, that correspond to lesions with a success of 94 up to 99%, in a sequence of the video-frames. The proposed methodology can be used as a valuable diagnostic tool that may assist physicians to identify possible tumor regions or malignant formations

Published in:

Image Processing, 2001. Proceedings. 2001 International Conference on  (Volume:2 )

Date of Conference:

7-10 Oct 2001