Stéphanie Bricq

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In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for(More)
Cutaneous blood flow (CBF) can be assessed non-invasively with lasers. Unfortunately, movement artefacts in the laser skin signal (LS(sk)) might sometimes compromise the interpretation of the data. To date, no method is available to remove movement artefacts point-by-point. Using a laser speckle contrast imager, we simultaneously recorded LS(sk) and the(More)
This paper proposes a new method to detect multiple sclerosis (MS) lesions on 3D multimodal brain MR images. MS lesions are detected as voxels that are not well explained by a statistical model for normal brain images. These outliers are extracted using the trimmed likelihood estimator (TLE). Spatial regularization is performed using a hidden Markov chain(More)
In this paper, we present a new automatic robust algorithm to segment multimodal brain MR images with Multiple Sclerosis (MS) lesions. The method performs tissue classification using a Hidden Markov Chain (HMC) model and detects MS lesions as outliers to the model. For this aim, we use the Trimmed Likelihood Estimator (TLE) to extract outliers. Furthermore,(More)
In this paper, we present a new automatic robust algorithm to segment multimodal brain MR images with Multiple Sclerosis (MS) lesions. The method performs tissue classification using a Hidden Markov Chain (HMC) model and detects MS lesions as outliers to the model. For this aim, we use the Trimmed Likelihood Estimator (TLE) to extract outliers. Furthermore,(More)
In this paper, we present a new Markovian scheme for MRI segmentation using a priori knowledge obtained from probability maps. Indeed we propose to use both triplet Markov chain and a brain atlas containing prior expectations about the spatial localization of the different tissue classes, to segment the brain in gray matter, white matter and cerebro-spinal(More)
For blood perfusion monitoring, laser speckle contrast (LSC) imaging is a recent non-contact technique that has the characteristic of delivering noise-like speckled images. To exploit LSC images for quantitative physiological measurements, we developed an approach that implements controlled spatial averaging to reduce the detrimental impact of the noise and(More)
In this paper we propose a mixed noise modeling so as to segment the brain and to detect lesion. Indeed, accurate segmentation of multimodal (T1, T2 and Flair) brain MR images is of great interest for many brain disorders but requires to efficiently manage multivariate correlated noise between available modalities. We addressed this problem in1 by proposing(More)
Background The diagnostic of left ventricular (LV) non compaction is still a challenge in clinical practice. There is a lack of reference method able to automatically quantify the total amount of LV trabeculations. Many algorithms have been proposed to determine endocardium and epicardium borders ranging from semi-automated to fully automated methods.(More)
The T1 and T2 relaxation times are the basic parameters behind magnetic resonance imaging. The accurate knowledge of the T1 and T2 values of tissues allows to perform quantitative imaging and to develop and optimize magnetic resonance sequences. A vast extent of methods and sequences has been developed to calculate the T1 and T2 relaxation times of(More)