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

Abstract

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.

Explainable artificial intelligence (XAI): closing the gap between image analysis and navigation in complex invasive diagnostic procedures

“…Despite having guidelines [ 15 , 17 ], cystoscopic findings are diverse and often challenging to classify [ 28 ]. Arriving at an accurate diagnosis is still largely dependent on the examiner’s experience, and the inter-observer variability is therefore wide [ 33 ]. Often in training, some spots on the bladder wall go undetected because of improper manipulation, especially during flexible cystoscopy.…”

“…in an attempt to minimise patient discomfort and ultimately increase acceptance rates, especially for follow-up examinations of non-muscle invasive bladder cancer (NMIBC) [31]. This also illustrates the need for a more comfortable procedure, as this procedure must be repeated frequently with regard to NMIBC, to detect recurrence and progression [32].…”

Detection of bladder cancer with feature fusion, transfer learning and CapsNets

“…Most recently a method was proposed but was tested in a small database without all tumor variants, which was only capable of identifying if the patient whether or not had tumor [18]. A texture-based approach was already addressed by the authors [19] with promising results but with the potential to be significantly improved by fusing color and texture features and refining the classification module, which is the proposed method in this paper. Also, the approach developed in [19] was only tested in a dataset with T1 tumors and normal images.…”

“…A texture-based approach was already addressed by the authors [19] with promising results but with the potential to be significantly improved by fusing color and texture features and refining the classification module, which is the proposed method in this paper. Also, the approach developed in [19] was only tested in a dataset with T1 tumors and normal images. Despite these recent approaches, none of them has been able to perform, yet, a complete and automatic diagnosis.…”

Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects

“…However, cystoscopic findings are diverse and often challenging to recognize and classify, ranging from healthy tissue to urothelial carcinoma [6]. A precise recognition of these features currently depends on the examiner´s skill and experience, leading to wide inter-observer variability in the interpretation of cystoscopic findings [7].…”