Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/52816

TitleAutomatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images
Author(s)Freitas, Nuno R.
Vieira, Pedro Miguel
Lima, Estêvão Augusto Rodrigues de
Lima, C. S.
KeywordsBladder tumor
Cystoscopy
Discrete wavelet transform
Multilayer perceptron
Segmentation
Issue date2018
PublisherIOP Publishing
JournalPhysics in Medicine and Biology
CitationFreitas, N. R., Vieira, P. M., Lima, E., & Lima, C. S. (2018, February 2). Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images. Physics in Medicine & Biology. IOP Publishing. http://doi.org/10.1088/1361-6560/aaa3af
Abstract(s)Correct classification of cystoscopy images depends on the interpreter's experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform '(DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value '(HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.
TypeArticle
URIhttps://hdl.handle.net/1822/52816
DOI10.1088/1361-6560/aaa3af
ISSN0031-9155
e-ISSN1361-6560
Peer-Reviewedyes
AccessOpen access
Appears in Collections:ICVS - Artigos em revistas internacionais / Papers in international journals
DEI - Artigos em revistas internacionais

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