Christophe Odet

A synchrotron radiation microtomography system for the analysis of trabecular bone samples.

X-ray computed microtomography is particularly well suited for studying trabecular bone architecture, which requires three-dimensional (3-D) images with high spatial resolution. For this purpose, we describe a three-dimensional computed microtomography (microCT) system using synchrotron radiation, developed at ESRF. Since synchrotron radiation provides a monochromatic and high photon flux x-ray beam, it allows high resolution and a high signal-to-noise ratio imaging. The principle of the system is based on truly three-dimensional parallel tomographic acquisition. It uses a two-dimensional (2-D) CCD-based detector to record 2-D radiographs of the transmitted beam through the sample under different angles of view. The 3-D tomographic reconstruction, performed by an exact 3-D filtered backprojection algorithm, yields 3-D images with cubic voxels. The spatial resolution of the detector was experimentally measured. For the application to bone investigation, the voxel size was set to 6.65 microm, and the experimental spatial resolution was found to be 11 microm. The reconstructed linear attenuation coefficient was calibrated from hydroxyapatite phantoms. Image processing tools are being developed to extract structural parameters quantifying trabecular bone architecture from the 3-D microCT images. First results on human trabecular bone samples are presented.

Automated 3D region growing algorithm based on an assessment function

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 our method is to build a region growing sequence by increasing the maximal homogeneity threshold from a very small value to a large one. On each segmented region, a 3D parameter that has been validated on a test image assesses the segmentation quality. This set of values called assessment function is used to determine of the optimal homogeneity criterion. Our 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 our algorithm performs better. Its main advantages are to eliminate isolated points due to the noise and to preserve connectivity of the bone structure.

Mammographic Texture Analysis: An Evaluation Of Risk For Developing Breast Cancer

This paper deals with early and accurate breast cancer risk assessment for women. The use of texture analysis tools for the eventual development of an automatic system is proposed. In a first step, a standard procedure for obtaining x-ray mammograms is set up, the resulting radiographic images then being classified into four risk groups by a specialist. In a second step, specific and selected texture algorithms using both global and local statistical properties of the images are implemented. A number of x-ray mammograms have been studied. One of the resulting important observations is that it seems inappropriate to define a set of distinct classes of risk; rather, an increasing gravity degree correlated to a continuous evolution of the mammographic textures from the lowest to the highest degree of risk is to be preferred. Finally, a systematic comparison between the human classification and the numerical coefficients provided by the texture analysis is performed. The coefficients do not allow risk classification by themselves. A critical examination of these preliminary results leads us to a constructive discussion concerning the future developments of the proposed method.

Two-dimensional spatial and temporal displacement and deformation field fitting from cardiac magnetic resonance tagging

Tagged magnetic resonance imaging is a specially developed technique to noninvasively assess contractile function of the heart. Several methods have been developed to estimate myocardial deformation from tagged image data. Most of these methods do not explicitly impose a continuity constraint through time although myocardial motion is a continuous physical phenomenon. In this paper, we propose to model the spatio-temporal myocardial displacement field by a cosine series model fitted to the entire tagged dataset. The method has been implemented in two dimensions (2D)+time. Its accuracy was successively evaluated on actual tagged data and on a simulated two-dimensional (2D) moving heart model. The simulations show that an overall theoretical mean accuracy of 0.1 mm can be attained with adequate model orders. The influence of the tagging pattern was evaluated and computing time is provided as a function of the model complexity and data size. This method provides an analytical and hierarchical model of the 2D+time deformation inside the myocardium. It was applied to actual tagged data from a healthy subject and from a patient with ischemia. The results demonstrate the adequacy of the proposed model for this evaluation.

Relevance of 2D radiographic texture analysis for the assessment of 3D bone micro-architecture.

Although the diagnosis of osteoporosis is mainly based on dual x-ray absorptiometry, it has been shown that trabecular bone micro-architecture is also an important factor in regard to fracture risk. In vivo, techniques based on high-resolution x-ray radiography associated to texture analysis have been proposed to investigate bone micro-architecture, but their relevance for giving pertinent 3D information is unclear. Thirty-three calcaneus and femoral neck bone samples including the cortical shells (diameter: 14mm, height: 30-40mm) were imaged using 3D-synchrotron x-ray micro-CT at the ESRF. The 3D reconstructed images with a cubic voxel size of 15μm were further used for two purposes: (1) quantification of three-dimensional trabecular bone micro-architecture, (2) simulation of realistic x-ray radiographs under different acquisition conditions. The simulated x-ray radiographs were then analyzed using a large variety of texture analysis methods (co-occurrence, spectral density, fractal, morphology, etc.). The range of micro-architecture parameters was in agreement with previous studies and rather large, suggesting that the population was representative. More than 350 texture parameters were tested. A small number of them were selected based on their correlation to micro-architectural morphometric parameters. Using this subset of texture parameters, multiple regression allowed one to predict up to 93% of the variance of micro-architecture parameters using three texture features. 2D texture features predicting 3D micro-architecture parameters other than BV/TV were identified. The methodology proposed for evaluating the relationships between 3D micro-architecture and 2D texture parameters may also be used for optimizing the conditions for radiographic imaging. Further work will include the application of the method to physical radiographs. In the future, this approach could be used in combination with DXA to refine osteoporosis diagnosis.

Scalable discrepancy measures for segmentation evaluation

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.

Image resolution enhancement using subpixel camera displacement

Abstract Given a CCD camera with its inherent resolution, we would like to get an image with improved resolution using camera displacement. An oversampled image is made by mixing several images with subpixel camera displacement. Finally, a deconvolution technique reduces the low pass filtering effects of the CCD cells, and so improved the image sharpness. The method is tested on computer simulations.

Image segmentation functional model

Abstract We propose a new approach of the image segmentation methods. This approach is based on a functional model composed of five elementary blocks called in an iterative process. Different segmentation methods can be decomposed with such a scheme and lead to elementary building blocks with unified functionality and interfaces. We present the decompositions of three segmentation methods and the implementation results, which illustrate the potential of the proposed model. This generic model is a common framework, which makes segmentation techniques more readable and offers new perspectives for the development, the comparison and the implementation of segmentation methods.

New discrepancy measures for segmentation evaluation

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.

OncoPET_DB: A Freely Distributed Database of Realistic Simulated Whole Body 18F-FDG PET Images for Oncology

The purpose of this paper is to generate and distribute a database of simulated whole body 18F-FDG positron emission tomography (PET) oncology images. As far as we know, this database is the first addressing the need for simulated 18F-FDG PET oncology images by providing a series of realistic whole-body patient images with well-controlled inserted lesions of calibrated uptakes. It also fulfills the requirements of detection performance studies by including normal and pathological cases. The originality of the database is based on three points. First, we built a complex model of 18F-FDG patient based on the Zubal phantom in combination with activity distributions in the main organs of interest derived from a series of 70 clinical cases. Secondly, we proposed a model of lesions extent corresponding to real lymphoma patients. The lesion contrast levels were derived from a human observer detection study so as to cover the entire range of detectability. Lastly, the simulated database was generated with the PET-SORTEO Monte Carlo simulation tool that was fully validated against the geometry of the ECAT EXACT HR+ (CTI/Siemens Knoxville). The oncoPET_DB database is composed of 100 whole-body PET simulated images, including 50 normal cases coming from different realizations of noise of the healthy model and 50 pathological cases including lesions of calibrated uptakes and various diameters. Such a database will be useful to evaluate algorithms that may impact quantification or contrast recovery, to perform observer studies or to assess computer-aided diagnosis methods. Perspectives include enriching the present database with new pathological and normal cases accounting for interindividual variability of anatomy and FDG uptake.