Vincent Barra

Automatic segmentation of subcortical brain structures in MR images using information fusion

Reports a new automated method for the segmentation of internal cerebral structures using an information fusion technique. The information is provided both by images and expert knowledge, and consists in morphological, topological, and tissue constitution data. All this ambiguous, complementary and redundant information is managed using a three-step fusion scheme based on fuzzy logic. The information is first modeled into a common theoretical frame managing its imprecision and incertitude. The models are then fused and a decision is taken in order to reduce the imprecision and to increase the certainty in the location of the structures. The whole process is illustrated on the segmentation of thalamus, putamen, and head of the caudate nucleus from expert knowledge and magnetic resonance images, in a protocol involving 14 healthy volunteers. The quantitative validation is achieved by comparing computed, manually segmented structures and published data by means of indexes assessing the accuracy of volume estimation and spatial location. Results suggest a consistent volume estimation with respect to the expert quantification and published data, and a high spatial similarity of the segmented and computed structures. This method is generic and applicable to any structure that can be defined by expert knowledge and morphological images.

A General Framework for the Fusion of Anatomical and Functional Medical Images

The collection of various data coming from anatomical and functional imagery is becoming very common for the study of a given pathology, and their aggregation generally allows for a better medical decision in clinical studies. A fusion process is described in this article for the modeling of this aggregation. The process is illustrated in the case of anatomical and functional images of the brain, but the general principle may be extended to other organs. The whole three-step fusion process based on possibilistic logic is detailed and a new class of fusion operator is introduced. The use of fuzziness in the process in general and in the operator in particular allows for the management of uncertainty and imprecision inherent to the images. The fusion process is illustrated in two clinical cases: the study of Alzheimers disease by MR/SPECT fusion and the study of epilepsy by MR/SPECT/PET fusion. Results are presented and evaluated, and a preliminary clinical validation is achieved. The assessment of the method is encouraging, allowing its application on several clinical problems.

Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation

An algorithm for the segmentation of a single sequence of three‐dimensional magnetic resonance (MR) images into cerebrospinal fluid, gray matter, and white matter classes is proposed. This new method is a possibilistic clustering algorithm using the fuzzy theory as frame and the wavelet coefficients of the voxels as features to be clustered. Fuzzy logic models the uncertainty and imprecision inherent in MR images of the brain, while the wavelet representation allows for both spatial and textural information. The procedure is fast, unsupervised, and totally independent of any statistical assumptions. The method is tested on a phantom image, then applied to normal and Alzheimers brains, and finally compared with another classic brain tissue segmentation method, affording a relevant classification of voxels into the different tissue classes. J. Magn. Reson. Imaging 2000;11:267–278.

Segmentation of fat and muscle from MR images of the thigh by a possibilistic clustering algorithm.

Physical training is proved to induce changes in physical capacity and body composition. We propose in this article a fast, unsupervised and fully three-dimensional automatic method to extract muscle and fat volumes from magnetic resonance images of thighs in order to assess these changes. The technique relies on the use of a fuzzy clustering algorithm and post-processings to accurately process the body composition of thighs. Results are compared on 11 healthy voluntary elderly people with those provided on the same data by a validated method already published, and its reliability is assessed on repeated measures on three subjects. The two methods statistically agree when computing muscle and fat volumes, and clinical implications of this fully automatic method are important for medicine, physical conditioning, weight-loss programs and predictions of optimal body weight.

Three-dimensional mapping of the lateral ventricles in autism

In this study, a computational mapping technique was used to examine the three-dimensional profile of the lateral ventricles in autism. T1-weighted three-dimensional magnetic resonance images of the brain were acquired from 20 males with autism (age: 10.1+/-3.5 years) and 22 male control subjects (age: 10.7+/-2.5 years). The lateral ventricles were delineated manually and ventricular volumes were compared between the two groups. Ventricular traces were also converted into statistical three-dimensional maps, based on anatomical surface meshes. These maps were used to visualize regional morphological differences in the thickness of the lateral ventricles between patients and controls. Although ventricular volumes measured using traditional methods did not differ significantly between groups, statistical surface maps revealed subtle, highly localized reductions in ventricular size in patients with autism in the left frontal and occipital horns. These localized reductions in the lateral ventricles may result from exaggerated brain growth early in life.

Automatic volumetric measurement of lateral ventricles on magnetic resonance images with correction of partial volume effects

To propose a method for the quantification of lateral ventricle (LV) volumes on a single sequence of 3D magnetic resonance (MR) images.

Benefit of endurance training in elderly people over a short period is reversible.

Abstract The present study assessed daily activity, physical capacity and body composition in 11 initially sedentary healthy subjects [5 men and 6 women, mean age 62.8 (SD 2.7) years] before training (To), after completion of 7 (T7w) and 14 (T14w) weeks of training, and again 6 (T6m) and 12 (T12m) months after training. The mean daily activity index decreased from T7w to T12m reaching a lower level than at To [T12m − To = −1.5 (SD 4.6) units, P = 0.18]. Mean maximal oxygen uptake (V˙O2max) and its corresponding mean power output (W˙max) were increased by 12.5 (SD 6.6)% (P = 0.003) and 22.8 (SD 12.8)% (P = 0.003), respectively, at T14w, and returned to their To levels within 1 year. Mean body mass (mb) remained stable until T6m but increased significantly by 2.6 (SD 3.7)% from T6m to T12m (P < 0.05). Mean fat mass (mf, from bioelectrical impedance analysis measurements) tended to decrease [−2.0 (SD 4.2)%, P = 0.10] during the training period but increased by 7.8 (SD 10.9)% between T6m and T12m (P < 0.05). The mean fat free mass did not vary during the study period (P = 0.81) but magnetic resonance imaging (MRI) showed that mean thigh muscle volume decreased between T7w and T12m to less than at To [T12m − To = −2.3 (SD 3.6)%, P = 0.05]. Therefore, this study confirmed the favourable effects of endurance training on the physical capacity and body composition of elderly people, but demonstrated that the training programme would have to be continued to maintain the training-related benefits (i.e. increased V˙O2max and W˙max) which would otherwise be lost within 1 year. After training, mb and mf were found to be increased. Furthermore, a fast and reproducible MRI protocol was validated for study of small intra-individual variations in tissue volumes in longitudinal studies.

Quantification of brain tissue volumes using MR/MR fusion

We propose in this article to compute White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CF) volumes from MR images of a human brain using possibilistic logic and a fusion scheme. The fusion process is divided into three steps: fuzzy tissue maps are first computed on all images using a possibilistic clustering algorithm. Fusion is then achieved for all tissues with a context based fusion operator. The final segmentation allows for the computation of brain tissue volumes. Applications on a brain model show a great agreement between the results and the true segmentation, thus affording a relevant tissue classification on clinical data.

Synthesis of a Functional Information with Anatomical Landmarks by Multiresolution Fusion of Brain Images

In order to help clinicians with the diagnosis of neurodegenerative diseases, we provide a synthetic functional information located in relation with anatomical structures. The final image is processed by multimodal data fusion between SPECT and MR images. We propose a new method for the management of such multiresolution data, in which a geometrical model allows an accurate correspondence of voxels from both images, while preserving at best both original pieces of information. We use this matching method to replace the interpolation step in the compulsory image registration of the data fusion process. The geometrical model is first built from registration parameters. Computational geometry algorithms, applied to this model, allow the computation of numerical values used to process the final information. The method has been applied to brain perfusion and neurotransmission SPECT images

Segmentation of the striatum using data fusion

Proposes a new segmentation scheme to detect cerebral structures in MRI acquisitions using numerical information contained in the image and expert knowledge brought by a specialist. This process is divided in three steps: first, information contained in the MR image is extracted using a fuzzy clustering algorithm, and theoretical information concerning the structure to segment is modeled using possibility theory. Information fusion is then processed, followed by a decision step ending the structure segmentation. Heads of caudate nuclei and putamens are segmented using this method. Results are promising and validation is performed using both numerical indexes and assessment by an expert. This method can be applied to any cerebral structure in an MR image, provided that it can be described in terms of shape, direction and distance by an expert and that the contrast and resolution of the MRI are sufficient.