Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model

Pereyra, Marcelo; Dobigeon, Nicolas; Batatia, Hadj; Tourneret, J.-Y.

doi:10.1109/tmi.2012.2190617

Cited by 57 publications

(56 citation statements)

References 63 publications

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“…Nowadays, many segmentation methods which could be split into different categories are widely used, for example: thresholding approaches, region-growing and region-merging approaches [24], classifiers, clustering approaches, Markov random field models, ANNs, deformable models [25,26], atlas-guided approaches [27], quadric functions with the implementation in order to avoid confusing between shadows and pigmented skin lesions [28], THz spectroscopic imaging and clustering algorithms [29], texture and fractal analysis [30,31], watershed technique [32], gradient vector flow algorithms [33] coherent analysis [34], melanoma recognition system method based on the ABCD criteria [35], support vector machines [36]. In addition to image segmentation tools as active contours and snakes, edge detection and clustering techniques based on thresholding were used [37].…”

Section: Methodsmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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Ultrasonic and digital dermatoscopy diagnostic methods are used in order to estimate the changes of structure, as well as to non-invasively measure the changes of parameters of lesions of human tissue. These days, it is very actual to perform the quantitative analysis of medical data, which allows to achieve the reliable early-stage diagnosis of lesions and help to save more lives. The proposed automatic statistical post-processing method based on integration of ultrasonic and digital dermatoscopy measurements is intended to estimate the parameters of malignant tumours, measure spatial dimensions (e.g. thickness) and shape, and perform faster diagnostics by increasing the accuracy of tumours differentiation. It leads to optimization of time-consuming analysis procedures of medical images and could be used as a reliable decision support tool in the field of dermatology.

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Section: Methodsmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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“…This strategy has been used successfully in several recent image processing applications (see [13], [14], [41]- [45] for examples in image filtering, dictionary learning, image reconstruction, fusion and segmentation). Many of these recent MCMC methods have been proposed for Bayesian models that include a Potts MRF [12], [13], [15], [16], [43]. However, these methods only studied the estimation of θ and z conditionally to a known granularity coefficient β.…”

Section: B Parameter Priors 1) Labelsmentioning

confidence: 99%

“…This observation model has been recently proposed to describe ultrasound images of dermis [53] and has been successfully applied to the segmentation of skin lesions in 3D ultrasound images [16]. Accordingly, the conditional observation model (1) used in the experiments is defined by an α-Rayleigh distribution…”

Section: B Mixture Of α-Rayleigh Distributionsmentioning

confidence: 99%

“…Ultrasound-based lesion inspection is an active topic in dermatological oncology, where patient treatment depends mainly on the depth of the lesion and the number of skin layers it has invaded. This problem has been recently addressed using an α-Rayleigh mixture model (25) coupled with a tridimensional Potts MRF as prior distribution for the class labels [16]. The algorithm investigated in [16] estimates the label vector and the mixture parameters conditionally to a known value of β that is set heuristically by cross-validation.…”

Section: B Lesion Segmentation In a 3d Ultrasound Imagementioning

confidence: 99%

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Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm

Pereyra

Dobigeon

Batatia

et al. 2013

IEEE Trans. on Image Process.

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This paper addresses the problem of estimating the Potts parameter β jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on β requires computing the intractable normalizing constant of the Potts model. In the proposed MCMC method the estimation of β is conducted using a likelihood-free Metropolis-Hastings algorithm. Experimental results obtained for synthetic data show that estimating β jointly with the other unknown parameters leads to estimation results that are as good as those obtained with the actual value of β. On the other hand, assuming that the value of β is known can degrade estimation performance significantly if this value is incorrect. To illustrate the interest of this method, the proposed algorithm is successfully applied to real bidimensional SAR and tridimensional ultrasound images.

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“…Interesting works [17][18][19][20][21][22][23] are the Bayesian methods for image segmentation from indirect data (inversionsegmentation) also based a Potts model for the labels. Nevertheless, the existing developments are not adapted for textures.…”

Section: Introductionmentioning

confidence: 99%

Deconvolution-segmentation for textured images

Giovannelli

Vacar

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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The paper tackles the problem of joint deconvolution and segmentation specifically for textured images. The images are composed of patches of textures that belong to a set of K possible classes. Each class of image is described by a Gaussian random field and the classes are modelled by a Potts field. The method relies on a hierarchical model and a Bayesian strategy to jointly estimate the labels, the textured images as well as the hyperparameters. An important point is that the parameter of the Potts field is also estimated. The estimators are designed in an optimal manner (marginal posterior maximizer for the labels and posterior mean for the other unknowns). They are computed based on a convergent procedure, from samples of the posterior obtained through an MCMC algorithm (Gibbs sampler including PerturbationOptimization). A first numerical evaluation provides encouraging results despite the strong difficulty of the problem.

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Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model

Cited by 57 publications

References 63 publications

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm

Deconvolution-segmentation for textured images

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