Biomedical subjects

B Macq

Publications and source records attributed to B Macq.

6 recordsLinked to original sources

PubMed2006

3D CT-based cephalometric analysis: 3D cephalometric theoretical concept and software.

INTRODUCTION: We present an original three-dimensional cephalometric analysis based on a transformation of a classical two dimensional topological cephalometry. METHODS: To validate the three-dimensional cephalometric CT based concept we systematically compared the alignments of anatomic structures. We used digital lateral radiography to perform the classical two-dimensional cephalometry, and a three-dimensional CT surface model for the three-dimensional cephalometry. RESULTS: Diagnoses based on both two-dimensional and three-dimensional analyses were adequate, but the three-dimensional analysis gave more information such as the possibility of comparing the right and left side of the skull. Also the anatomic structures were not superimposed which improved the visibility of the reference landmarks. CONCLUSION: We demonstrated that three-dimensional analysis gives the same results as two-dimensional analysis using the same skull. We also present possible applications of the method.

Cephalometry↗

PubMed2006

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images.

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.

Brain↗

PubMed2001

Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model.

We present a new algorithm for the nonrigid registration of three-dimensional magnetic resonance (MR) intraoperative image sequences showing brain shift. The algorithm tracks key surfaces of objects (cortical surface and the lateral ventricles) in the image sequence using a deformable surface matching algorithm. The volumetric deformation field of the objects is then inferred from the displacements at the boundary surfaces using a linear elastic biomechanical finite-element model. Two experiments on synthetic image sequences are presented, as well as an initial experiment on intraoperative MR images showing brain shift. The results of the registration algorithm show a good correlation of the internal brain structures after deformation, and a good capability of measuring surface as well as subsurface shift. We measured distances between landmarks in the deformed initial image and the corresponding landmarks in the target scan. Cortical surface shifts of up to 10 mm and subsurface shifts of up to 6 mm were recovered with an accuracy of 1 mm or less and 3 mm or less respectively.

Algorithms↗

PubMed2000

Automatic morphometry of nerve histological sections.

A method for the automatic segmentation, recognition and measurement of neuronal myelinated fibers in nerve histological sections is presented. In this method, the fiber parameters i.e. perimeter, area, position of the fiber and myelin sheath thickness are automatically computed. Obliquity of the sections may be taken into account. First, the image is thresholded to provide a coarse classification between myelin and non-myelin pixels. Next, the resulting binary image is further simplified using connected morphological operators. By applying semantic rules to the zonal graph axon candidates are identified. Those are either isolated or still connected. Then, separation of connected fibers is performed by evaluating myelin sheath thickness around each candidate area with an Euclidean distance transformation. Finally, properties of each detected fiber are computed and false positives are removed. The accuracy of the method is assessed by evaluating missed detection, false positive ratio and comparing the results to the manual procedure with sampling. In the evaluated nerve surface, a 0.9% of false positives was found, along with 6.36% of missed detections. The resulting histograms show strong correlation with those obtained by manual measure. The noise introduced by this method is significantly lower than the intrinsic sampling variability. This automatic method constitutes an original tool for morphometrical analysis.

Animals↗

PubMed1998

Interactive DICOM image transmission and telediagnosis over the European ATM network.

The European High-Performance Information Infrastructure in Medicine, n(o)B3014 (HIM3) project of the Trans-European Network--Integrated Broadband Communications (TEN-IBC) program, started on March 1996 and finished on February 1997, aimed to test the medical usability of the European asynchronous transfer mode (ATM) network in medical image transmission. The Department of Radiology, University of Pisa, Pisa, Italy, and St-Luc University Hospital, Brussels, Belgium, involved in the project as healthcare partners in the radiological domain, established several connection sessions finalized to test the usability of Digital Imaging and Communication (DICOM) image transmission and interactive telediagnosis tools in the daily radiological practice. The Pisa site was connected to the Italian ATM pilot (Sirius Network) through the Tuscany metropolitan area network (MAN), while St-Luc University Hospital was connected to Belgium ATM network through the Brussels MAN. By means of international connections provided by the European JAMES project, a link between the two sites was established, connecting both national ATM networks. Due to the large variety of hardware present in the medical centers, multiplatform software tools were used and tested: central test node (CTN) release 2.8 [3], VAT [6], NV-3.3 [7], and IDI (UCL homemade multiplatform teleradiology tool for interactive visualization and processing of DICOM images). During the telediagnosis session, lead by radiologists in both hospitals, each site submitted neuroradiological clinical cases to the other for remote consultation. The connection, available for a period of two weeks, at 2-Mbit/s bandwidth, allowed the transmission of MR images (256 x 256 x 12 bit) and simultaneous multimedia interactive discussion of the cases. Both off-line transmission and review of the images, using the CTN DICOM transfer routines, and on-line interactive image discussion, using the IDI telediagnosis software, were tested successfully from the technical and medical point of view.

Computer Communication Networks↗

PubMed1996

Morphological feature extraction for the classification of digital images of cancerous tissues.

This paper presents a new method for automatic recognition of cancerous tissues from an image of a microscopic section. Based on the shape and the size analysis of the observed cells, this method provides the physician with nonsubjective numerical values for four criteria of malignancy. This automatic approach is based on mathematical morphology, and more specifically on the use of Geodesy. This technique is used first to remove the background noise from the image and then to operate a segmentation of the nuclei of the cells and an analysis of their shape, their size, and their texture. From the values of the extracted criteria, an automatic classification of the image (cancerous or not) is finally operated.

Biopsy↗