Jean Sequeira

SHAPE AND TEXTURE INDEXES APPLICATION TO CELL NUCLEI CLASSIFICATION

This paper describes the sequence of construction of a cell nuclei classification model by the analysis, the characterization and the classification of shape and texture. We describe first the elaboration of dedicated shape indexes and second the construction of the associated classification submodel. Then we present a new method of texture characterization, based on the construction and the analysis of statistical matrices encoding the texture. The various characterization techniques developed in this paper are systematically compared to previous approaches. In particular, we paid special attention to the results obtained by a versatile classification method using a large range of descriptors dedicated to the characterization of shapes and textures. Finally, the last classifier built with our methods achieved 88% of classification out of the 94% possible.

A discrete model for color naming

The ability to associate labels to colors is very natural for human beings. Though, this apparently simple task hides very complex and still unsolved problems, spreading over many different disciplines ranging from neurophysiology to psychology and imaging. In this paper, we propose a discrete model for computational color categorization and naming. Starting from the 424 color specimens of the OSA-UCS set, we propose a fuzzy partitioning of the color space. Each of the 11 basic color categories identified by Berlin and Kay is modeled as a fuzzy set whose membership function is implicitly defined by fitting the model to the results of an ad hoc psychophysical experiment (Experiment 1). Each OSA-UCS sample is represented by a feature vector whose components are the memberships to the different categories. The discrete model consists of a three-dimensional Delaunay triangulation of the CIELAB color space which associates each OSA-UCS sample to a vertex of a 3D tetrahedron. Linear interpolation is used to estimate the membership values of any other point in the color space. Model validation is performed both directly, through the comparison of the predicted membership values to the subjective counterparts, as evaluated via another psychophysical test (Experiment 2), and indirectly, through the investigation of its exploitability for image segmentation. The model has proved to be successful in both cases, providing an estimation of the membership values in good agreement with the subjective measures as well as a semantically meaningful color-based segmentation map.

Geometrical modelling of abdominal aortic aneurysms

Stent graft combination devices have been developed as a new alternative for treating abdominal aortic aneurysms. The major risk using this new technique with standard devices is the perigraft leak. In order to choose a suitable graft for each patient, and thus avoid such a risk, we have developed a program which provides three-dimensional representations of such aneurysms.

Three-dimensional modeling of tree-like anatomical structures

In this paper a method for creating geometrical models of tree-like anatomical structures is described. This method is basically interactive and thus it takes advantage of the users expertise to define highly-structured models even when using nonhomogeneous data sets. First, tubular cavities are reconstructed sequentially; then, junctions between these cavities are provided in such a way that resulting models are continuously shaped (we characterize this property by the G1-continuity (i.e., a tangent plane can be defined at any point on the surface).

Medical image segmentation using texture directional features

Medical image segmentation can often be performed through tissue texture analysis. One of the most recent and interesting ideas to do that is to take into account the distribution of local maximum orders. We have followed up this idea by using directional maximums and we have applied it to tissue differentiation. Two problems are emerging now: one is the identification of a given texture (labeling) and another one is the characterization of the different areas within images (segmentation). In this paper, we present our new approach for texture representation and analysis, and we point out the advances and problems involved in the image segmentation process.

Earth observation using radar data: an overview of applications and challenges

Abstract The first pictures of the earth were taken from a balloon in the mid-19th century and thus started ‘earth observation’. Aerial missions in the 20th century enabled the build-up of outstanding photographic libraries and then with Landsat-1, the first civilian satellite launched in 1972, digital images of the earth became an operational reality. The main roles of earth observation have become scientific, economic and strategic, and the role of synthetic aperture radar (SAR) is significant in this overall framework. Radar image exploitation has matured and several operational programs regularly use SAR data for input and numerous applications are being further developed. The technological development of interferometry and polarimetry has helped further develop these radar based applications. This paper highlights this role through a description of actual applications and projects, and concludes with a discussion of some challenges for which SAR systems may provide significant assistance.

Arc of ellipse detection for video image registration

In this paper, we give a detailed presentation of a robust algorithm for detecting arcs of ellipse in a binary image. The characterization of such arcs of ellipse enables the identification between some video image elements and the corresponding landmarks in a 3D model of the scene to be represented. This algorithm is based on a classical ellipse property that enables its parameters separation. It provides interesting results even in noisy images or when these arcs are small and partially hidden.

CLOSED FREE-FORM SURFACE GEOMETRICAL MODELING A NEW APPROACH WITH GLOBAL AND LOCAL CHARACTERIZATION

In this paper, we present a new approach to geometrical modeling which allows the user to easily characterize and control the shape defined to a closed surface. We will focus on dealing with the shapes topological, morphological and geometrical properties separately. To do this, we have based our work on the following observations concerning surfaces defined by control-points, and implicit surfaces with skeleton. They both provide complementary approaches to the surfaces deformation, and both have specific advantages and limits. We thus attempted to conceive a model which integrates the local and geometrical characterization induced by the control points, as well as the representation of the morphology given by the skeleton. Knowing that the lattice of control points is close to the surface and that the skeleton is centered in the related shape, we thought of a 3-layer model. The transition layer separates the local geometrical considerations from those linked to the global morphology. We apply our model to shape design in order to modify an object in an interactive and ergonomic way, as well as to reconstruction which allows better shape understanding. To do so, we present the algorithms related to these processes.

A Robust 3-D IVUS Transducer Tracking Using Single-Plane Cineangiography

During an intravascular ultrasound (IVUS) intervention, a catheter with an ultrasound transducer is introduced in the body through a blood vessel, and then, pulled back to image a sequence of vessel cross sections. Unfortunately, there is no 3-D information about the position and orientation of these cross-section planes, which makes them less informative. To position the IVUS images in space, some researchers have proposed complex stereoscopic procedures relying on biplane angiography to get two X-ray image sequences of the IVUS transducer trajectory along the catheter. To simplify this procedure, we and others have elaborated algorithms to recover the transducer 3-D trajectory with only a single view X-ray image sequence. In this paper, we present an improved method that provides both automated 2-D and 3-D transducer tracking based on pullback speed as a priori information. The proposed algorithm is robust to erratic pullback speed and is more accurate than the previous single-plane 3-D tracking methods.

ParSymG: a parallel clustering approach for unsupervised classification of remotely sensed imagery

ABSTRACT Symmetry is a common feature in the real world. It may be used to improve a classification by using the point symmetry-based distance as a measure of clustering. However, it is time consuming to calculate the point symmetry-based distance. Although an efficient parallel point symmetry-based K-means algorithm (ParSym) has been propsed to overcome this limitation, ParSym may get stuck in sub-optimal solutions due to the K-means technique it used. In this study, we proposed a novel parallel point symmetry-based genetic clustering (ParSymG) algorithm for unsupervised classification. The genetic algorithm was introduced to overcome the sub-optimization problem caused by inappropriate selection of initial centroids in ParSym. A message passing interface (MPI) was used to implement the distributed master–slave paradigm. To make the algorithm more time-efficient, a three-phase speedup strategy was adopted for population initialization, image partition, and kd-tree structure-based nearest neighbor searching. The advantages of ParSymG over existing ParSym and parallel K-means (PKM) alogithms were demonstrated through case studies using three different types of remotely sensed images. Results in speedup and time gain proved the excellent scalability of the ParSymG algorithm.