Automatic skeletal muscle segmentation through random walks and graph-based seed placement

International audienceIn this paper we propose a novel skeletal muscle segmentation method driven from discrete optimization. We introduce a graphical model that is able to automatically determine appropriate seed positions with respect to the different muscle classes. This is achieved by taking into account the expected local visual and geometric properties of the seeds through a pair-wise Markov Random Field. The outcome of this optimization process is fed to a powerful graphbased diffusion segmentation method (random walker) that is able to produce very promising results through a fully automated approach. Validation on challenging data sets demonstrates the potentials of our method

Prior Knowledge, Random Walks and Human Skeletal Muscle Segmentation

International audienceIn this paper, we propose a novel approach for segmenting the skeletal muscles in MRI automatically. In order to deal with the absence of contrast between the different muscle classes, we proposed a principled mathematical formulation that integrates prior knowledge with a random walks graph-based formulation. Prior knowledge is repre- sented using a statistical shape atlas that once coupled with the random walks segmentation leads to an efficient iterative linear optimization sys- tem. We reveal the potential of our approach on a challenging set of real clinical data

Discriminative Parameter Estimation for Random Walks Segmentation

The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Speci cally, they provide a hard segmentation of the images, instead of a proba- bilistic segmentation. We overcome this challenge by treating the opti- mal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach signi cantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.Comment: Medical Image Computing and Computer Assisted Interventaion (2013

Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report

The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Speci cally, they provide a hard segmentation of the images, instead of a proba-bilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach signi cantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles

Application of texture analysis to muscle MRI: 1-What kind of information should be expected from texture analysis?

Several previous clinical or preclinical studies using computerized texture analysis of MR Images have demonstrated much more clinical discrimination than visual image analysis by the radiologist. In muscular dystrophy, a discriminating power has been already demonstrated with various methods of texture analysis of magnetic resonance images (MRI-TA). Unfortunately, a scale gap exists between the spatial resolutions of histological and MR images making a direct correlation impossible. Furthermore, the effect of the various histological modifications on the gray level of each pixel is complex and cannot be easily analyzed. Consequently, clinicians will not accept the use of MRI-TA in routine practice if TA remains a “black box” without clinical correspondence at a tissue level. A goal therefore of the multicenter European COST action MYO-MRI is to optimize MRI-TA methods in muscular dystrophy and to elucidate the histological meaning of MRI textures.info:eu-repo/semantics/publishedVersio

Globalização económica e fragmentação geopolítica : a caminho de um mundo de equilíbrios instáveis e temporários?

Este texto explora a ideia de que a evolução mundial nos próximos anos vai ser marcada pela interacção complexa entre, por um lado as tensões associadas à Globalização da Economia Mundial, e por outro as Incertezas em torno da Fragmentação Geopolítica Mundial. Começa por identificar os grandes processos envolvidos na primeira "força motriz" - uma ampliação e "regionalização" da Economia Mundial; uma dinâmica de Globalização económica; uma competição acesa entre "Modelos de Capitalísmo"; uma mutação tecnológica abrangente, que modifica as estruturas económicas e a posição relativa das economias; e por último uma regulação económica global que procura responder à acumulação de tensões geradas pela interacção dos processos anteriores. Seguidamente identifica alguns processos chave que organizam a segunda" força motriz", como sejam o avanço da democratização, decorrendo em paralelo com a sobreposição de crises profundas em diversos Estados; um processo de fragmentação e "regionalização" em termos geopolíticos e de segurança; uma alteração na relação de forças entre potências, que está ainda numa fase inconclusiva; uma mutação tecnológica militar que pode influenciar decisivamente essa alteração; e a manifestação de dificuldades na regulação estratégica e geopolítica mundial, pela interacção dos processos anteriores e no contexto da ultrapassagem dos mecanismos de regulação típicos da guerra fria. Por último o texto ilustra algumas das interacções que se podem estabelecer entre as dinâmicas das duas "forças motrizes" sem explorar em profundidade o tema

Uniform and textured regions separation in natural images towards mpm adaptive denoising

Abstract. Natural images consist of texture, structure and smooth regions and this makes the task of filtering challenging mainly when it aims at edge and texture preservation. In this paper, we present a novel adaptive filtering technique based on a partition of the image to ”noisy smooth zones ” and ”texture or edge + noise ” zones. To this end, an analysis of local features is used to recover a statistical model that associates to each pixel a probability measure corresponding to a membership degree for each class. This probability function is then encoded in a new denoising process based on a MPM (Marginal Posterior Mode) estimation technique. The posterior density is computed through a non parametric density estimation method with variable kernel bandwidth that aims to adapt the denoising process to image structure. In our algorithm the selection of the bandwidth relies on a non linear function of the membership probabilities. Encouraging, experimental results demonstrate the potential of our approach.