Thomas Grenier

3D Robust Adaptive Region Growing for segmenting [18F] fluoride ion PET images

We propose a new robust adaptive region growing method (RoAd RG) based on two local parameters: the local mean value of the intensity function and the local mean value of the norm of the intensity gradient. This approach enables a better spread of the region growing inside the region of interest while avoiding the merge of outlier pixels. We tested our method on a synthesized noisy image, and demonstrated that RoAd RG gives better result than non adaptive or not fully adaptive methods. We applied positively our method to 3D [18F]fluoride ion PET images for segmenting bone structures, and showed its superiority compared to a non adaptive method.

Hybrid Approach for Multiparametric Mean Shift Filtering

In ultrasound imaging, robustness of the diagnosis can be improved by using many images of parameters. In this paper we propose a hybrid approach for improving multi-parametric mean shift filtering (MPMS). Multi-parametric filtering is really attractive since it works conjointly in the spatial-range domain, taking into account the spatial location of the data as well as the range values of many parameters. Hybrid MPMS is an. iterative method that combines two mean shift procedures called nonblurring and blurring. Our method was positively tested on a set of simulated ultrasound data. The results show the superiority of hybrid MPMS compared to the simple MPMS filtering.

Automated seeds location for whole body NaF PET segmentation

18F-labeled NaF, also called [/sup 18/F] fluoride ion, positron emission tomography (NaF PET) is a specific imaging modality of bone activity and allows bone tumor detection. In this paper, we propose a fast-automated method to locate anatomical structures by special planes and seeds in whole body NaF PET images. This step is crucial in registration and segmentation processes such as region growing. Our method proceeds in two steps: first, it delineates anatomical objects such as bladder, head, spine and legs with two horizontal planes (bottom and top); secondly, it determines labeled seeds in these objects. Processes are based on the analysis of the slice energy (SE) and the computation of the center of gravity (CG) of all transverse slices. This method was applied to initialize seeds in order to segment the whole skeleton. This segmentation is necessary to detect bone tumors and quantify bone uptake. Results are fast and quite satisfying. Robustness of our method was positively tested on a set of eight NaF PET images.

Unsupervised spatio-temporal filtering of image sequences. A mean-shift specification

We propose an unsupervised spatio-temporal image filtering method based on mean-shift.We adapt the spatial and feature range domains to handle temporal evolution.A constraint is added on the samples evolution to select temporal neighbors.Our method outperforms standard mean-shift by adequately considering time information.Effectiveness is demonstrated on both synthetic and real brain MRI time-series data. A new spatio-temporal filtering scheme based on the mean-shift procedure, which computes unsupervised spatio-temporal filtering for univariate feature evolution, is proposed in this paper. Our main contributions are on one hand the modification of the spatial/range domains to appropriately integrate the temporal feature into the mean-shift iterative form and on the other hand the addition of a trajectory constraint in the feature space with the use of the infinity norm. Therefore, only the samples living the same life in the feature space will converge. Major assets of the standard mean-shift framework such as convergence and bandwidth parameters adjustment are preserved. In this paper, we study the relative importance of the bandwidth parameters and the efficiency of the proposed method is assessed on synthetic data and compared to the standard mean-shift framework for spatio-temporal data filtering. The obtained results have allowed us to undertake a first study on real data, which has led to encouraging results in identifying spatio-temporal evolution of multiple sclerosis lesions appearing on T2-weighted MR images.

Rapid Tomography in Environmental TEM: How Fast Can We Go to Follow the 3D Evolution of Nanomaterials in situ?

Environmental Transmission Electron Microscopy (ETEM) in a dedicated instrument has been the subject of recent considerable developments allowing to follow chemical reactions in situ under environmental, e.g. gas and temperature conditions even at atomic resolution. A typical domain of applications concerns catalysis, where supported nanoparticles (NPs) are followed during calcination, oxidation or reduction in the presence of various gases. Whereas numerous works have now been published in conventional imaging, that is, 2D projection, little work is reported on 3D investigations performed in situ. It is easy to understand why such an approach is difficult: since TEM tomography consists in reconstructing numerically a tilt series of projections over a wide angular range, the time to acquire these data is generally too important as compared to the speed of the sample evolution, or the kinetics of the studied chemical reaction. The duration of the conventional acquisition step is typically one hour, or a fraction of one hour, making it impossible to get tilting sequences where the object does not experience any significant shape change which obviously hampers 3D reconstruction. This trivial statement leads to the conclusion that speeding up the acquisition procedure is the key of Environmental Tomography. This contribution summarizes where we are in developing Fast Tomography in Bright Field (E)TEM at the minute and even second level mainly for applications on nano-catalysts. Experiments are performed in a FEI Titan ETEM and using a Wildfire heating holder from DENS Solutions allowing ±70° tilt with S5-type MEMS-based SiNx chips.

Locally controlled regularized spatiotemporal anisotropic diffusion

In this paper, we propose a new anisotropic diffusion formulation allowing non-linear spatiotemporal filtering of image sequences. We first formulate a multidimensional spatiotemporal diffusion equation based on Barashs iterative form, processing independently both spatial, temporal and intensity dimensions with their own diffusion functions and scale parameters. We then introduce a local regularization term designed to smooth the remaining spike noise. Experimental results processed on synthetic data and real MR images show that considering the temporal information and the regularization term improves the filtering quality. The method is also shown to be robust to noise, blur and temporal intensity evolution. Results are compared to the BM3D method with MSE and SSIM evaluation metrics.

Evaluation of noise and blur effects with SIRT-FISTA-TV reconstruction algorithm: Application to fast environmental transmission electron tomography

Fast tomography in Environmental Transmission Electron Microscopy (ETEM) is of a great interest for in situ experiments where it allows to observe 3D real-time evolution of nanomaterials under operating conditions. In this context, we are working on speeding up the acquisition step to a few seconds mainly with applications on nanocatalysts. In order to accomplish such rapid acquisitions of the required tilt series of projections, a modern 4K high-speed camera is used, that can capture up to 100 images per second in a 2K binning mode. However, due to the fast rotation of the sample during the tilt procedure, noise and blur effects may occur in many projections which in turn would lead to poor quality reconstructions. Blurred projections make classical reconstruction algorithms inappropriate and require the use of prior information. In this work, a regularized algebraic reconstruction algorithm named SIRT-FISTA-TV is proposed. The performance of this algorithm using blurred data is studied by means of a numerical blur introduced into simulated images series to mimic possible mechanical instabilities/drifts during fast acquisitions. We also present reconstruction results from noisy data to show the robustness of the algorithm to noise. Finally, we show reconstructions with experimental datasets and we demonstrate the interest of fast tomography with an ultra-fast acquisition performed under environmental conditions, i.e. gas and temperature, in the ETEM. Compared to classically used SIRT and SART approaches, our proposed SIRT-FISTA-TV reconstruction algorithm provides higher quality tomograms allowing easier segmentation of the reconstructed volume for a better final processing and analysis.

Spatial and spectral regularization for multispectral photoacoustic image clustering

Photoacoustic imaging is a hybrid modality used to image biological tissues. Multispectral optical excitation permits to obtain functional images thanks to the tissue specific optical absorption that depends on the light wavelength. The aim of this study is to propose a clustering method for photoacoustic multispectral images based on both spatial neighbourhood and spectral behaviour. The proposed methodology is adapted from spatio-temporal mean-shift approach: it clusters distant or neighbouring patterns having similar spectral profiles. The clustering performance of our modified mean-shift algorithm is experimentally tested on multispectral photoacoustic tomography data. Results obtained from phantoms including two blood dilutions and colored absorbers are presented. It is thus shown that our strategy allows the experimental discrimination of media, achieving a clustering performance of more than 99%. Moreover, depending on the applied pre-processing the discrimination of different concentrations of a same medium is possible.

Region Growing: When Simplicity Meets Theory – Region Growing Revisited in Feature Space and Variational Framework

Region growing is one of the most intuitive techniques for image segmentation. Starting from one or more seeds, it seeks to extract meaningful objects by iteratively aggregating surrounding pixels. Starting from this simple description, we propose to show how region growing technique can be elevated to the same rank as more recent and sophisticated methods. Two formalisms are presented to describe the process. The first one derived from non-parametric estimation relies upon feature space and kernel functions. The second one is issued from a variational framework, describing the region evolution as a process which minimizes an energy functional. It thus proves the convergence of the process and takes advantage of the huge amount of work already done on energy functionals. In the last part, we illustrate the interest of both formalisms in the context of life imaging. Three segmentation applications are considered using various modalities such as whole body PET imaging, small animal μCT imaging and experimental Synchrotron Radiation μCT imaging. We will thus demonstrate that region growing has reached this last decade a maturation that offers many perspectives of applications to the method.

A fully automatic and multi-structural segmentation of the left ventricle and the myocardium on highly heterogeneous 2D echocardiographic data

2D echocardiography remains nowadays the main clinical imaging modality in daily practice for assessing the cardiac function. This task requires an accurate segmentation of the left ventricle (LV) and myocardium at end diastole (ED) and systole (ES). Because of intrinsic high variability in image quality in ultrasound data, manual interactions are still needed to obtain a precise delineation of the heart structures. This is both time consuming for specialists and not reproducible. In this study, we investigate a machine learning solution based on the Structured Random Forest algorithm to fully automate the segmentation of the myocardium and LV on heterogeneous clinical data. We compare its performance to the semi-automatic state of the art Active Appearance Model (AAM). The competitive results that were achieved lead us to believe that supervised learning may be the key to automatic heart segmentation.