Using cystoscopy to segment bladder tumors with a multivariate approach in different color spaces

Abstract

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.

Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes

“…Colour characteristics have been shown to significantly improve tumour classification and segmentation (Freitas et al 2017). To acquire merged PET/CT images, the colour overlay technique was applied, whereby the grayscale CT and PET images are converted to colour images by colour table (Zhu and Nortmann, 2011).…”

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

“…A partition algorithm improves initial estimates leading to local maxima closer to the global maximum being a usual procedure in practice. The entire algorithm is explained more in-depth in Freitas et al (2017aFreitas et al ( , 2017b. Some advantages of the HSV color space domain are low correlation and perceptual motivation.…”

“…In previous works, the authors applied segmentation approaches to separate tumor from normal tissue in individual images taken from cystoscopy exams. This was done with a color-based analysis, considering that the distribution of the intensities of different color channels is modeled by a Gaussian mixture model, whose parameters can be found by using the EM algorithm (Freitas et al 2017a(Freitas et al , 2017b. Although, due to the repetitive change in the gray values and structures of the textures, traditional detection methods based on intensity or edge detection are ineffective because textures are realizations of non-Gaussian stationary processes.…”

“…All steps are based on high frequency texture information obtained from filtering the frame in the wavelet domain. Since previous attempts to segment tumor frames (Freitas et al 2017a(Freitas et al , 2017b did not perform optimally for T1 tumor aspects a preprocessing, which computes an image with only higher frequency content, allowed substantial improvements in segmentation. The segmentation module uses the maximum a posteriori (MAP) approach and the expectation-maximization (EM) algorithm.…”

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

“…In Germany, BCa represents 4.7% of all new cancer cases and 3.2% of all cancer-related deaths [ 3 ]. Despite a low stage and grade, recurrence rates range from 50 to 75% at 20 years, setting patients at risk of progression to muscle-invasive disease [ 4 ]. Periodic cystoscopic examinations remain the cornerstone of patient follow-up to detect early recurrences and reduce the risk of progression [ 5 ].…”