210 research outputs found
Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model
This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm
This paper addresses the problem of estimating the Potts parameter B 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 B requires computing the intractable
normalizing constant of the Potts model. In the proposed MCMC method the
estimation of B is conducted using a likelihood-free Metropolis-Hastings
algorithm. Experimental results obtained for synthetic data show that
estimating B jointly with the other unknown parameters leads to estimation
results that are as good as those obtained with the actual value of B. On the
other hand, assuming that the value of B 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
Vision-based techniques for gait recognition
Global security concerns have raised a proliferation of video surveillance
devices. Intelligent surveillance systems seek to discover possible threats
automatically and raise alerts. Being able to identify the surveyed object can
help determine its threat level. The current generation of devices provide
digital video data to be analysed for time varying features to assist in the
identification process. Commonly, people queue up to access a facility and
approach a video camera in full frontal view. In this environment, a variety of
biometrics are available - for example, gait which includes temporal features
like stride period. Gait can be measured unobtrusively at a distance. The video
data will also include face features, which are short-range biometrics. In this
way, one can combine biometrics naturally using one set of data. In this paper
we survey current techniques of gait recognition and modelling with the
environment in which the research was conducted. We also discuss in detail the
issues arising from deriving gait data, such as perspective and occlusion
effects, together with the associated computer vision challenges of reliable
tracking of human movement. Then, after highlighting these issues and
challenges related to gait processing, we proceed to discuss the frameworks
combining gait with other biometrics. We then provide motivations for a novel
paradigm in biometrics-based human recognition, i.e. the use of the
fronto-normal view of gait as a far-range biometrics combined with biometrics
operating at a near distance
Fractal analysis of laplacian pyramidal filters applied to segmentation of soil images
The laplacian pyramid is a well-known technique for image processing in which local operators of many scales, but identical shape, serve as the basis functions. The required properties to the pyramidal filter produce a family of filters, which is unipara metrical in the case of the classical problem, when the length of the filter is 5. We pay attention to gaussian and fractal behaviour of these basis functions (or filters), and we determine the gaussian and fractal ranges in the case of single parameter ?. These fractal filters loose less energy in every step of the laplacian pyramid, and we apply this property to get threshold values for segmenting soil images, and then evaluate their porosity. Also, we evaluate our results by comparing them with the Otsu algorithm threshold values, and conclude that our algorithm produce reliable test results
Motion Textures: Modeling, Classification, and Segmentation Using Mixed-State Markov Random Fields
published_or_final_versio
Statistical modeling and processing of high frequency ultrasound images: application to dermatologic oncology
Cette thĂšse Ă©tudie le traitement statistique des images dâultrasons de haute frĂ©quence, avec application Ă lâexploration in-vivo de la peau humaine et lâĂ©valuation non invasive de lĂ©sions. Des mĂ©thodes BayĂ©siennes sont considĂ©rĂ©es pour la segmentation dâimages Ă©chographiques de la peau. On y Ă©tablit que les ultrasons rĂ©trodiffusĂ©s par la peau convergent vers un processus alĂ©atoire complexe de type Levy-Flight, avec des statistiques non Gaussiennes alpha-stables. Lâenveloppe du signal suit une distribution Rayleigh gĂ©nĂ©ralisĂ©e Ă queue lourde. A partir de ces rĂ©sultats, il est proposĂ© de modĂ©liser lâimage ultrason de multiples tissus comme un mĂ©lange spatialement cohĂ©rent de lois Rayleigh Ă queues lourdes. La cohĂ©rence spatiale inhĂ©rente aux tissus biologiques est modĂ©lisĂ©e par un champ alĂ©atoire de Potts-Markov pour reprĂ©senter la dĂ©pendance locale entre les composantes du mĂ©lange. Un algorithme BayĂ©sien original combinĂ© Ă une mĂ©thode Monte Carlo par chaine de Markov (MCMC) est proposĂ© pour conjointement estimer les paramĂštres du modĂšle et classifier chaque voxel dans un tissu. Lâapproche proposĂ©e est appliquĂ©e avec succĂšs Ă la segmentation de tumeurs de la peau in-vivo dans des images dâultrasons de haute frĂ©quence en 2D et 3D. Cette mĂ©thode est ensuite Ă©tendue en incluant lâestimation du paramĂštre B de rĂ©gularisation du champ de Potts dans la chaine MCMC. Les mĂ©thodes MCMC classiques ne sont pas directement applicables Ă ce problĂšme car la vraisemblance du champ de Potts ne peut pas ĂȘtre Ă©valuĂ©e. Ce problĂšme difficile est traitĂ© en adoptant un algorithme Metropolis-Hastings âsans vraisemblanceâ fondĂ© sur la statistique suffisante du Potts. La mĂ©thode de segmentation non supervisĂ©e, ainsi dĂ©veloppĂ©e, est appliquĂ©e avec succĂšs Ă des images Ă©chographiques 3D. Finalement, le problĂšme du calcul de la borne de Cramer-Rao (CRB) du paramĂštre B est Ă©tudiĂ©. Cette borne dĂ©pend des dĂ©rivĂ©es de la constante de normalisation du modĂšle de Potts, dont le calcul est infaisable. Ce problĂšme est rĂ©solu en proposant un algorithme Monte Carlo original, qui est appliquĂ© avec succĂšs au calcul de la borne CRB des modĂšles dâIsing et de Potts. ABSTRACT : This thesis studies statistical image processing of high frequency ultrasound imaging, with application to in-vivo exploration of human skin and noninvasive lesion assessment. More precisely, Bayesian methods are considered in order to perform tissue segmentation in ultrasound images of skin. It is established that ultrasound signals backscattered from skin tissues converge to a complex Levy Flight random process with non-Gaussian _-stable statistics. The envelope signal follows a generalized (heavy-tailed) Rayleigh distribution. Based on these results, it is proposed to model the distribution of multiple-tissue ultrasound images as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by a Potts Markov random field. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. The proposed method is successfully applied to the segmentation of in-vivo skin tumors in high frequency 2D and 3D ultrasound images. This method is subsequently extended by including the estimation of the Potts regularization parameter B within the Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because the likelihood of B is intractable. This difficulty is addressed by using a likelihood-free Metropolis-Hastings algorithm based on the sufficient statistic of the Potts model. The resulting unsupervised segmentation method is successfully applied to tridimensional ultrasound images. Finally, the problem of computing the Cramer-Rao bound (CRB) of B is studied. The CRB depends on the derivatives of the intractable normalizing constant of the Potts model. This is resolved by proposing an original Monte Carlo algorithm, which is successfully applied to compute the CRB of the Ising and Potts models
Combining local-physical and global-statistical models for sequential deformable shape from motion
The final publication is available at link.springer.comIn this paper, we simultaneously estimate camera pose and non-rigid 3D shape from a monocular video, using a sequential solution that combines local and global representations. We model the object as an ensemble of particles, each ruled by the linear equation of the Newton's second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency. The resulting approach allows to sequentially estimate shape and camera poses, while progressively learning a global low-rank model of the shape that is fed back into the optimization scheme, introducing thus, global constraints. The overall combination of local (physical) and global (statistical) constraints yields a solution that is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, without requiring any training data at all. Validation is done in a variety of real application domains, including articulated and non-rigid motion, both for continuous and discontinuous shapes. Our on-line methodology yields significantly more accurate reconstructions than competing sequential approaches, being even comparable to the more computationally demanding batch methods.Peer ReviewedPostprint (author's final draft
Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach
Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of
many adults. It a ects almost 1:5 - 5% of the general population. Sub-
Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of
morbidity and mortality. Therefore, radiologists aim to detect it and diagnose
it at an early stage, by analyzing the medical images, to prevent or reduce its
damages.
The analysis process is traditionally done manually. However, with the
emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are
adopted in the clinics to overcome the traditional process disadvantages, as
the dependency of the radiologist's experience, the inter and intra observation
variability, the increase in the probability of error which increases consequently
with the growing number of medical images to be analyzed, and the artifacts
added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA,
etc.) which impedes the radiologist' s work.
Due to the aforementioned reasons, many research works propose di erent
segmentation approaches to automate the analysis process of detecting a CA
using complementary segmentation techniques; but due to the challenging task
of developing a robust reproducible reliable algorithm to detect CA regardless
of its shape, size, and location from a variety of the acquisition methods, a
diversity of proposed and developed approaches exist which still su er from
some limitations.
This thesis aims to contribute in this research area by adopting two promising
techniques based on the multiresolution and statistical approaches in the
Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform
(CT), which empowers the segmentation by extracting features not apparent
in the normal image scale. While the second technique is the Hidden
Markov Random Field model with Expectation Maximization (HMRF-EM),
which segments the image based on the relationship of the neighboring pixels
in the contourlet domain.
The developed algorithm reveals promising results on the four tested Three-
Dimensional Rotational Angiography (3D RA) datasets, where an objective
and a subjective evaluation are carried out. For the objective evaluation, six
performance metrics are adopted which are: accuracy, Dice Similarity Index
(DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city,
and sensitivity. As for the subjective evaluation, one expert and four observers
with some medical background are involved to assess the segmentation visually.
Both evaluations compare the segmented volumes against the ground
truth data
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