Segmentation of bladder tumors in cystoscopy images using a MAP approach in different color spaces

Nowadays the diagnosis of bladder lesions relies upon cystoscopic examination and depend 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 pigmentations, but none uses white light cystoscopy images. Traditional cystoscopic images processing has a huge potential to improve early tumor detection and allow a more effective treatment. In this paper is described an initial approach to do segmentation of bladder cystoscopic images. This approach will be used in the future to automatically detect these types of lesions. It can be assumed that each region has a normal distribution with specific parameters, leading to the assumption that the distribution of intensities is a Gaussian Mixture Model (GMM). The most common bladder tumor type, with a cauliflower shape, appears with higher intensity than normal regions. The segmentation of these images is based on a Maximum A Posteriori (MAP) approach depending on pixel intensities of each three RGB and HSV channels, using the Expectation-Maximization (EM) algorithm to estimate the best GMM parameters. Experimental results show that the proposed method does bladder tumor segmentation in a more efficient way in RGB color space than in HSV, even in cases where the tumor shape is not well defined. Results also show that the channels with best results are the R component from RGB and the V component from HSV.This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941 and with the grant SFRH/BD/92143/2013.info:eu-repo/semantics/publishedVersio

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

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.This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941 and with the grant SFRH/BD/92143/2013.info:eu-repo/semantics/publishedVersio