Jean-Louis Dillenseger

Quaternion Zernike moments and their invariants for color image analysis and object recognition

Moments and moment invariants have become a powerful tool in pattern recognition and image analysis. Conventional methods to deal with color images are based on RGB decomposition or graying, which may lose some significant color information. In this paper, by using the algebra of quaternions, we introduce the quaternion Zernike moments (QZMs) to deal with the color images in a holistic manner. It is shown that the QZMs can be obtained from the conventional Zernike moments of each channel. We also provide the theoretical framework to construct a set of combined invariants with respect to rotation, scaling and translation (RST) transformation. Experimental results are provided to illustrate the efficiency of the proposed descriptors.

A moment-based three-dimensional edge operator

A three-dimensional edge operator for detecting anatomical structures in medical imaging is presented. It uses the spatial moments of the gray-level surface, and operates in three dimensions with any window size. It allows the location and the contrast surface, as well as the surface orientation, to be estimated. The computation of the discrete version is reported. Bias and errors due to the spatial sampling and noise are analyzed at both a theoretical and experimental level. The moment-based operator is compared with other well-known edge operators using simple shaped primitives for which the analytical solution is known. The 3-D rendering of real data is then provided by merging the operator in a ray-tracing framework. >

Wavelet Analysis of EEG for Three-Dimensional Mapping of Epileptic Events

This paper is aimed at understanding epileptic patient disorders through the analysis of surface electroencephalograms (EEG). It deals with the detection of spikes or spike-waves based on a nonorthogonal wavelet transform. A multilevel structure is described that locates the temporal segments where abnormal events occur. These events are then visually interpreted by means of a 3D mapping technique. This 3D display makes use of a ray tracing scheme and combines both the functional (the EEG but also its wavelet representation) and the morphological data (acquired from computed tomography [CT] or magnetic resonance imaging [MRI] devices). The results show that a significant reduction of the clinical workload is obtained while the most important episodes are better reviewed and analyzed.

Liver Vessels Segmentation Using a Hybrid Geometrical Moments/Graph Cuts Method

This paper describes a fast and fully automatic method for liver vessel segmentation on computerized tomography scan preoperative images. The basis of this method is the introduction of a 3-D geometrical moment-based detector of cylindrical shapes within the minimum-cut/maximum-flow energy minimization framework. This method represents an original way to introduce a data term as a constraint into the widely used Boykovs graph cuts algorithm, and hence, to automate the segmentation. The method is evaluated and compared with others on a synthetic dataset. Finally, the relevancy of our method regarding the planning of a necessarily accurate percutaneous high-intensity focused ultrasound surgical operation is demonstrated with some examples.

A 3-D moment based approach for blood vessel detection and quantification in MRA

This paper describes a new method for the three-dimensional (3-D) tracking and the quantification of blood vessels from magnetic resonance angiography (MRA). The approach is based on 3-D geometrical moments and consists of the following steps: (1) interactive selection of 3-D seed points; (2) automatic tracking of the vessels; (3) local computation of both diameter and orientation; (4) rendering of the vessels. This detection and estimation scheme has been validated on simulated and real data.

Fast simulation of ultrasound images from a CT volume

The goal of our work is to propose a fast ultrasound image simulation from CT volumes. This method is based on a model elaborated by Bamber and Dickinson that predict the appearance and properties of a B-Scan ultrasound image from the distribution of point scatterers. We propose to extend this model for the standard medical ultrasound image simulation by taking into account the acoustical tissue properties (scatterer distribution) and the geometry and the specifications of the ultrasound probe (circular probe, number and size of transducers, US pulse frequency and bandwidth, etc.). Simulations have been obtained in a fairly fast computation speed and qualitatively they present most of the real ultrasound image characteristics.

Image-Guided Therapy: Evolution and Breakthrough [A Look At]

Many questions remain open in most of the physics-based therapies that we have have been shortly reviewed. All are based on principles discovered a long time ago, and advances are still far from being sufficient. Clinical studies do not clarify enough how and when they should be used in first intention or sequenced over time. In other words, patients must be cared today and not tomorrow. There is no doubt that these therapies offer many opportunities. New probe and transducer design and device miniaturization through microelectromechanical systems (MEMS) technology will converge, with the parallel evolution of medical robots, imaging modalities, and drug delivery advances. Almost all, if not all, physical therapeutic systems described here are basically image guided and can be merged into the medical robot frame. Dual-sensing and actuating (or activating) devices are in progress, and even if there is a long way from the initial concepts to clinical applications (with inherent quality control and traceability), the trend toward more efficient, less-invasive care will support the efforts made in the field. In parallel, basic research opens new windows. For instance, terahertz (THz) imaging (with a frequency range lying between the infrared and microwave regions of the spectrum) is just emerging as a biomedical modality. We do not know if, in the future, it may not have therapeutic applications.

Spherical Harmonics Based Intrasubject 3-D Kidney Modeling/Registration Technique Applied on Partial Information

This paper presents a three-dimensional (3-D) shape reconstruction/intrapatient rigid registration technique used to establish a Nephron-Sparing Surgery preoperative planning. The usual preoperative imaging system is the Spiral CT Urography, which provides successive 3-D acquisitions of complementary information on kidney anatomy. Because the kidney is difficult to demarcate from the liver or from the spleen only limited information on its volume or surface is available. In this paper, we propose a methodology allowing a global kidney spatial representation on a spherical harmonics basis. The spherical harmonics are exploited to recover the kidney 3-D shape and also to perform intrapatient 3-D rigid registration. An evaluation performed on synthetic data showed that this technique presented lower performance then expected for the 3-D shape recovering but exhibited registration results slightly more accurate as the iterative closest point technique with faster computation time

Fast detection and characterization of vessels in very large 3-D data sets using geometrical moments

An improved and very fast algorithm dealing with the extraction of vessels in three-dimensional imaging is described. The approach is based on geometrical moments and a local cylindrical approximation. A robust estimation of vessel and background intensity levels, position, orientation, and diameter of the vessels with adaptive control of key parameters, is provided during vessel tracking. Experimental results are presented for lower limb arteries in multidetector computed tomography scanner.

A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model

The CT uroscan consists of three to four time-spaced acquisitions of the same patient. After registration of these acquisitions, the data forms a volume in which each voxel contains a vector of elements corresponding to the information of the CT uroscan acquisitions. In this paper we will present a segmentation tool in order to differentiate the anatomical structures within the vectorial volume. Because of the partial volume effect (PVE), soft segmentation is better suited because it allows regions or classes to overlap. Gaussian mixture model is often used in statistical classifier to realize soft segmentation by getting classes probability distributions. But this model relies only on the intensity distributions, which will lead a misclassification on the boundaries and on inhomogeneous regions with noise. In order to solve this problem, a neighborhood weighted Gaussian mixture model is proposed in this paper. Expectation maximization algorithm is used as optimization method. The experiments demonstrate that the proposed method can get a better classification result and is less affected by the noise.