Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/52926

TitleUsing cystoscopy to segment bladder tumors with a multivariate approach in different color spaces
Author(s)Freitas, Nuno R.
Vieira, Pedro Miguel
Lima, Estêvão Augusto Rodrigues de
Lima, C. S.
Issue date13-Sep-2017
PublisherIEEE
JournalProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Abstract(s)Nowadays the diagnosis of bladder lesions relies upon cystoscopy examination and depends on the interpreter's experience. State of the art of bladder tumor identification are based on 3D reconstruction, using CT images (Virtual Cystoscopy) or images where the structures are exalted with the use of pigmentation, but none uses white light cystoscopy images. An initial attempt to automatically identify tumoral tissue was already developed by the authors and this paper will develop this idea. Traditional cystoscopy images processing has a huge potential to improve early tumor detection and allows a more effective treatment. In this paper is described a multivariate approach to do segmentation of bladder cystoscopy images, that will be used to automatically detect and improve physician diagnose. Each region can be assumed as a normal distribution with specific parameters, leading to the assumption that the distribution of intensities is a Gaussian Mixture Model (GMM). Region of high grade and low grade tumors, usually appears with higher intensity than normal regions. This paper proposes a Maximum a Posteriori (MAP) approach based on pixel intensities read simultaneously in different color channels from RGB, HSV and CIELab color spaces. The Expectation-Maximization (EM) algorithm is used to estimate the best multivariate GMM parameters. Experimental results show that the proposed method does bladder tumor segmentation into two classes in a more efficient way in RGB even in cases where the tumor shape is not well defined. Results also show that the elimination of component L from CIELab color space does not allow definition of the tumor shape.
TypeconferencePaper
URIhttp://hdl.handle.net/1822/52926
ISBN978-1-5090-2809-2
DOI10.1109/EMBC.2017.8036910
ISSN1557-170X
Peer-Reviewedyes
AccessrestrictedAccess
Appears in Collections:DEI - Artigos em atas de congressos internacionais

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