Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

Correct cystoscopy images classification depends on interpreter's experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, wherefore the automatic identification of tumors has a significant role in the early stage diagnosis and its accuracy. To our best knowledge the use of white light cystoscopy images to bladder tumor diagnosis wasn't reported so far. In this paper a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change tissue texture. As it is well accepted by the current scientific community texture information is more present in the medium to high frequency range which can be selected by using the Discrete Wavelet Transform (DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided in 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. A Multilayer Perceptron (MLP) and Support Vector Machine (SVM), with a stratified 10-fold cross-validation procedure wereas used for classification purposes by using the hue-saturation-value (HSV), red-green-blue (RGB), and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both pre-processing and classification steps based on DWT. The proposed method can achieve good performances 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.

Physics in medicine and biology. 2017 Dec 22 [Epub ahead of print]

Nuno Renato Freitas, Pedro M Vieira, Estevão Lima, Carlos S Lima

CMEMS-UMinho Research Unit, University of Minho, Campus of Azurém, Guimarães, 4800-058, PORTUGAL., CMEMS-UMinho, University of Minho, Braga, PORTUGAL., School of Medicine, Life and Health Sciences Research Institute, ICVS/3B's - Associate Lab, Braga, PORTUGAL., CMEMS-UMinho Research Unit, University of Minho, Guimarães, PORTUGAL.