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

Freitas, Nuno R.; Vieira, Pedro; Lima, Estêvão; Lima, C. S.

doi:10.1109/enbeng.2017.7889429

Cited by 3 publications

(5 citation statements)

References 15 publications

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“…To do this, the usual way is to consider that a local maximum is reached when the content of each cluster does not change in consecutive iterations. The pdf of a multivariate normal distribution modeling each class can be computed by equation ( 5) (Vieira et al 2012, Freitas et al 2017a.…”

Section: Segmentationmentioning

confidence: 99%

“…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.…”

Section: Segmentationmentioning

confidence: 99%

“…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.…”

Section: Automatic T1 Bladder Tumor Detection By Using Wavelet Analys...mentioning

confidence: 99%

“…The main goal of the segmentation process is to locate lesion tissue to be processed separately from normal tissue. The segmentation algorithm used in these experiments was a multivariate MAP approach, based on Bayes' rule (4) (Freitas et al 2017a), that indicates how the a posteriori probability, p(C i |x), of each class is calculated. Once the objective is to separate abnormal from normal tissue, a two Gaussian model is appropriate.…”

Section: Segmentationmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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

Freitas¹,

Vieira²,

Lima³

et al. 2018

Phys. Med. Biol.

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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.

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Section: Segmentationmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

Section: Automatic T1 Bladder Tumor Detection By Using Wavelet Analys...mentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

mentioning

confidence: 99%

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

Freitas¹,

Vieira²,

Lima³

et al. 2018

Phys. Med. Biol.

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

Freitas

Vieira

Lima

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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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.

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Automated detection and extraction of skull from MR head images: Preliminary results

Göçeri

Songul

2017

2017 International Conference on Computer Science and Engineering (UBMK)

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

Cited by 3 publications

References 15 publications

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

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

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

Automated detection and extraction of skull from MR head images: Preliminary results

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