A MRI myocardial perfusion analysis tool.

MRI myocardial perfusion analysis is an important element for the ischemic heart disorder assessment. The spatio-temporal analysis of the myocardial raising during the first crossing of a contrast bolus allows to identify the ischemic or hypoperfused areas. Such an analysis requires an accurate tracking of the myocardium on the whole sequence and a robust segmentation to identify pathological and healthy regions inside the myocardium. In this paper, we present a semiautomatic tracking tool and a segmentation algorithm based on statistical tests and a recent concentration theorem. We show experimental results for both the tracking step and the segmentation of hypoperfused areas to confirm the availability of this setting.

[Cranial analysis for early treatment and the biodynamic concept of cranio-facial morphogenesis]. / Une analyse crânienne au service des traitements précoces et du concept biodynamique de la morphogenèse cranio-faciale.

The understanding of the background of a malocclusion is essential especially in the cranial field. Statistics reinforce the choice of our cranial landmarks and the main lines of our cranial biometry which is really 3D. It marks the relationships between the cranium and the face. It expresses the relevance of diagnosing and treating under 6 years of age.

Acquisition, segmentation and tracking of the cerebral vascular tree on 3D magnetic resonance angiography images.

This paper presents a method for the detection, representation and visualisation of the cerebral vascular tree and its application to magnetic resonance angiography (MRA) images. The detection method is an iterative tracking of the vessel centreline with subvoxel accuracy and precise orientation estimation. This tracking algorithm deals with forks. Centrelines of the vessels are modelled by second-order B-spline. This method is used to obtain a high-level description of the whole vascular network. Applications to real angiographic data are presented. An MRA sequence has been designed, and a global segmentation of the whole vascular tree is realised in three steps. Applications of this work are accurate 3D representation of the vessel centreline and of the vascular tree, and visualisation. The tracking process is also successfully applied to 3D contrast enhanced MR digital subtracted angiography (3D-CE-MRA) of the inferior member vessels. In addition, detection of artery stenosis for routine clinical use is possible due to the high precision of the tracking algorithm.

Segmentation of color images from serous cytology for automated cell classification.

OBJECTIVE: To design an automated system for the classification of cells based on analysis of serous cytology, with the aim of segmenting both cytoplasm and nucleus using color information from the images as the main characteristic of the cells. STUDY DESIGN: The segmentation strategy uses color information coupled with mathematical morphology tools, such as watersheds. Cytoplasm and nuclei of all diagnostic cells are retained; erythrocytes and debris are eliminated. Special techniques are used for the separation of clustered cells. RESULTS: A large set of cells was assessed by experts to score the segmentation success rate. All cells were segmented whatever their spatial configurations. The average success rate was 92.5% for nuclei and 91.1% for cytoplasm. CONCLUSION: This color information-based segmentation of images of serous cells is accurate and provides a useful tool. This segmentation strategy will improve the automated classification of cells.

Detection and statistical analysis of human cortical sulci.

Many studies dealing with the human brain use the spatial coordinate system of brain anatomy to localize functional regions. Unfortunately, brain anatomy, and especially cortical sulci, is characterized by a high interindividual variability. Specific tools called anatomical atlases must then be considered to make the interpretation of anatomical examinations easier. The work described here first aims at building a numerical atlas of the main cortical sulci. Our system is based on a database containing a collection of anatomical MRI of healthy volunteer brains. Their sulci have been manually drawn and labeled for both hemispheres. Sulci are represented as 3D superficial curves. After a nonlinear registration process, a statistical atlas of the cortical topography of a particular MRI is built from the database. It is an a priori model of cortical sulci, including three major components: an average curve represents the average shape and position of each sulcus; a search area accounts for its spatial variation domain; a set of quantitative parameters describes the variability of sulci geometry and topology. This atlas is completely individualized and adapted to the features of the brain under examination. The atlas is represented by a graph, the nodes of which represent sulci and the edges the relations between sulci. It can also be considered a statistical model that describes the cortical topography as well as its variability.

MRI geometric distortion: a simple approach to correcting the effects of non-linear gradient fields.

We present a method to correct intensity variations and voxel shifts caused by non-linear gradient fields in magnetic resonance images. The principal sources of distortion are briefly discussed, as well as the methods of correction currently in use. The implication of the gradient field non-linearities on the signal equations are described in a detailed way for the case of two- and three-dimensional Fourier imaging. A model of these non-linearities, derived from the geometry of the gradient coils, is proposed and then applied in post-processing to correct any images regardless of the acquisition sequence. Initial position errors, as large as 4 mm (i.e., four voxels of 1 x 1 x 1.4 mm3) before correction, are reduced to less than the voxel sizes after correction.

[Neighborhood graphs and image processing. Contribution to images of immunohistochemical staining]. / Graphes de voisinage et traitement d'images. Application à des images de marquage immunohistochimique.

Devising an image analyzer dedicated to the automatic quantification of immunohistochemical staining for clinical oncology implies developing a method for the delimitation of tumoral cell nests, setting aside tumoral stroma, while accounting for the topology of the staining. The representation of images by neighborhood graphs can bring an answer to both requirements. In this paper, a methodological approach is presented. It consists in a preliminary study dealing with nuclear immunostaining images of breast cancer. Segmentation of the graph structure allows to separate clusters of cancer cells and the analysis of this structure can account for the focal or diffuse aspect of the staining within the tumor.