Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/17658

TitleTexture classification of images from endoscopic capsule by using MLP and SVM – a comparative approach
AuthorLima, C. S.
Correia, J. H.
Ramos, J.
Babosa, Daniel
KeywordsCapsule endoscopy
Texture analysis
Discrete wavelet transform
Multilayer perceptrons
Support vector machines
Issue date12-Sep-2009
PublisherSpringer
AbstractThis article reports a comparative study of Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) in the classification of endoscopic capsule images. Texture information is coded by second order statistics of color image levels extracted from co-occurrence matrices. The co-occurrence matrices are computed from images rich in texture information. These images are obtained by processing the original images in the wavelet domain in order to select the most important information concerning texture description. Texture descriptors calculated from co-occurrence matrices are then modeled by using third and forth order moments in order to cope with non-Gaussianity, which appears especially in some pathological cases. Several color spaces are used, namely the most simple RGB, the most related to the human perception HSV, and the one that best separates light and color information, which uses luminance and color differences, usually known as YCbCr.
TypeconferenceObject
URIhttp://hdl.handle.net/1822/17658
Publisher versionhttp://science.icmcc.org/2009/09/07/world-congress-on-medical-physics-and-biomedical-engineering/
Peer-Reviewedyes
Appears in Collections:DEI - Artigos em atas de congressos internacionais

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