A method to semi-automatically segment the pulmonary vascular tree from computed axial tomography (CAT) images is presented. The main goal is to aid the diagnosis and treatment of acute respiratory distress syndrome and pulmonary embolism. The proposed methodology is based on a variational region growing method and a multi-scale vessel enhancement filter. Preliminary studies were made with a 20 CAT image dataset that included healthy and pathological lung scans. The results were satisfactory, although in some cases vessels were not correctly distinguished from other thin structures such as mucus-filled bronchi, nodules and airway walls connected to vessels.

Magnetic Resonance Imaging (MRI) is a medical imaging modality that provides structural and functional information. To improve the contrast of MR images, contrast agents, such as Ultrasmall SuperParamagnetic Iron Oxide particles (USPIO), are more commonly being used. Quantifying and locating these nanoparticles is of high interest. An efficient technique consists in images analyzing of the default field inhomogeneities induced by USPIOs. In a previous work, we have introduced such a quantification framework. Here, we improve our approach by deriving from two methods: Wiener and CLS filtering. Both methods have been evaluated on realistic data demonstrating that CLS filtering gives very promising quantification results.

Region growing has become a popular method for 3D segmentation. Starting from a seed, this approach allows one to extract a region by merging all its neighbors and comparing the extracted region to a reference. Here, we present an alternative approach to constrain the evolution of the region growing method in respect to a fixed reference shape. This approach is based on a shape description by the Tchebichef moments. The evolution equation minimizes a function that represents the distance between the evolving region and the reference shape. Experimental results show the ability of our method to drive the segmentation towards a desired shape in 2D and 3D data. Finally, we apply our shape prior conjointly to a bimodal segmentation functional, showing its benefits on segmentation results.

We present an adaptive solution for guinea pig knee cartilage segmentation using a 3-D smoothing B-Spline active surface. An adaptive parametric combination of edge-based forces and balloon force solves the problem of capture range of external forces. The comparison between the results of the experiments using this method and previous 3-D validated snake segmentation shows that the accuracy and robustness are improved.

A new adaptive IIR filtering of surface meshes by smoothing B-spline is proposed. We define some ad hoc processing of borders and singular points for genus 0 meshes. By adapting the filter cut-off frequency to the local mesh sampling, it enables homogeneous smoothing of non-uniform meshes. We present genus 0 and genus 1 meshes filtering as well as volume segmentations. These segmentations have been obtained using the IIR filtering of deformation forces within a smoothing B-Spline active surface.

We propose a new automated region growing method integrating shape prior (RGISP). The aim of this work is to improve region growing segmentation by taking into account a reference model. Our algorithm is assessed on a synthesized image and compared with two other methods in order to point up the contribution of shape prior. It was also applied to segment in-vivo mu-CT images of mouse kidneys in the framework of small animal imaging. RGISP gives promising results and appears to be well adapted to satisfy small animal imaging constraints.

In this paper, we propose an original correction method for susceptibility artifact in spin echo and gradient echo MR images when a field map of the imaged object is known. The susceptibility artifact impact on MR images is modeled by means of a pixel shifting map and an intensity distortion map. An original scheme based on MRI simulation is used to build the intensity distortion map to take into account different complex phenomena such as intravoxel dephasing. The proposed method has been successfully tested on both spin echo and gradient echo MR images giving promising results.

In this paper, a new active surface based segmentation algorithm is proposed. We detail the regularization process that is conducted through a smoothing B-spline filtering of the mesh point displacements. The corresponding segmentation algorithm is presented and applied on medical data. The proposed segmentation method is fast and suitable to quadrangular meshes.

In this paper, we give an overview of a python language library named "DaVaW" designed to ease the development of medical image processing applications. The main originality of this library is to provide high level components like medical image browser, 3D viewers or ROI manager, as well as a programming pattern that enables communication and interaction between these components. This library has an open source distribution and is mainly based on VTK and WxPython libraries.

In this paper, we present the implementation of a Magnetic Resonance Imaging (MRI) simulator on a GRID computing architecture. The simulation process is based on the resolution of Bloch equation [1] in a 3D space. The computation kernel of the simulator is distributed to the grid nodes using MPICH-G2 [2]. The results presented show that simulation of 3D MRI data is achieved with a reasonable cost which gives new perspectives to MRI simulations usage.

In this paper, we propose new evaluation measures for scene segmentation results, which are based on computing the difference between a region extracted from a segmentation map and the corresponding one on an ideal segmentation. The proposed measures take into account separately both under and over detected pixels. It also associates in its computation the compactness of the region under investigation.

We propose a set of scalable discrepancy measures that may be applied for segmentation evaluation when a reference is known. The proposed measures take into account under and over detected points within an adjustable area. They give the intensity of the discrepancy and its relative position. Furthermore a scale parameter allows the accuracy of the measures to be adjusted.

A new region growing algorithm is proposed for the automated segmentation of three-dimensional images. No initial parameters such as the homogeneity threshold or the seeds location have to be adjusted. The principle of the authors' method is to build a region growing sequence in increasing the maximal homogeneity threshold from a very small value to large one. On each segmented region, a 3D parameter which has been validated on a test image, evaluates the segmentation quality. This set of values called evaluation function is used to the determination of the best segmentation. The authors' algorithm was tested on 3D MR images for the segmentation of trabecular bone samples in order to quantify osteoporosis. A comparison to automated and manual thresholding showed that the authors' algorithm performs better. Its main advantages are to eliminate isolated points due to the noise and to preserve connectivity of the bone structure.

The influence of the phase of conjugate quadrature filters (CQFs) on the performances of a subband coding scheme is analyzed. When the filter length is short, the phase characteristic has virtually no influence on the peak signal-to-noise ratio (PSNR)/bit rate results and on the performance of postprocessing algorithms such as edge detection.

Many applications require the acquisition of 3D CT images made of a stack of 2D X- ray slices. The different slices are obtained by the displacement of the patient table, which sets the resolution in the z direction perpendicularly to the slice plane. This resolution is physically limited by the detector width, on which the X-ray radiation is integrated. Those detectors inherently carry out a low pass filtering process and aliasing effects. As a consequence the reconstruction of a slice in any direction can present artefacts. Then we propose to apply a superresolution technique based on a deconvolution method associated to oversampling using subpixel motion in order to improve the resolution in the z-direction. This method requires the acquisition of overlapping slices which are later processed directly in the spatial domain, what is particularly interesting for computer time. Its application provides thinner and accurate new slices. After the presentation of the method, the results obtained on experimental images coming from a conventional CT scanner are presented.