The prospects for application of computational anatomy in forensic anthropology for sex determination.

The purpose of this study is to assess the relevance of computational anatomy for the sex determination in forensic anthropology. A novel groupwise registration algorithm is used, based on keypoint extraction, able to register several hundred full body images in a common space. Experiments were conducted on 83 CT scanners of living individuals from the public VISCERAL database. In our experiments, we first verified that the well-known criteria for sex discrimination on the hip-bone were well preserved in mean images. In a second experiment, we have tested semi-automatic positioning of anatomical landmarks to measure the relevance of groupwise registration for future research. We applied the Probabilistic Sex Diagnosis tool on the predicted landmarks. This resulted in 62% of correct sex determinations, 37% of undetermined cases, and 1% of errors. The main limiting factors are the population sample size and the lack of precision for the initial manual positioning of the landmarks in the mean image. We also give insights on future works for robust and fully automatic sex determination.

Probabilistic models and numerical calculation of system matrix and sensitivity in list-mode MLEM 3D reconstruction of Compton camera images.

This paper addresses the problem of evaluating the system matrix and the sensitivity for iterative reconstruction in Compton camera imaging. Proposed models and numerical calculation strategies are compared through the influence they have on the three-dimensional reconstructed images. The study attempts to address four questions. First, it proposes an analytic model for the system matrix. Second, it suggests a method for its numerical validation with Monte Carlo simulated data. Third, it compares analytical models of the sensitivity factors with Monte Carlo simulated values. Finally, it shows how the system matrix and the sensitivity calculation strategies influence the quality of the reconstructed images.

Fast multidimensional NMR spectroscopy for sparse spectra.

Multidimensional NMR spectroscopy is widely used for studies of molecular and biomolecular structure. A major disadvantage of multidimensional NMR is the long acquisition time which, regardless of sensitivity considerations, may be needed to obtain the final multidimensional frequency domain coefficients. In this article, a method for under-sampling multidimensional NMR acquisition of sparse spectra is presented. The approach is presented in the case of two-dimensional NMR acquisitions. It relies on prior knowledge about the support in the two-dimensional frequency domain to recover an over-determined system from the under-determined system induced in the linear acquisition model when under-sampled acquisitions are performed. This over-determined system can then be solved with linear least squares. The prior knowledge is obtained efficiently at a low cost from the one-dimensional NMR acquisition, which is generally acquired as a first step in multidimensional NMR. If this one-dimensional acquisition is intrinsically sparse, it is possible to reconstruct the corresponding two-dimensional acquisition from far fewer observations than those imposed by the Nyquist criterion, and subsequently to reduce the acquisition time. Further improvements are obtained by optimizing the sampling procedure for the least-squares reconstruction using the sequential backward selection algorithm. Theoretical and experimental results are given in the case of a traditional acquisition scheme, which demonstrate reliable and fast reconstructions with acceleration factors in the range 3-6. The proposed method outperforms the CS methods (OMP, L1) in terms of the reconstruction performance, implementation and computation time. The approach can be easily extended to higher dimensions and spectroscopic imaging.

Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and Graph Cut.

We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analyses and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated, and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared with a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.

Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing.

Compressive sensing (CS) theory makes it possible - under certain assumptions - to recover a signal or an image sampled below the Nyquist sampling limit. In medical ultrasound imaging, CS could allow lowering the amount of acquired data needed to reconstruct the echographic image. CS thus offers the perspective of speeding up echographic acquisitions and could have many applications, e.g. triplex acquisitions for CFM/B-mode/Doppler imaging, high-frame-rate echocardiography, 3D imaging using matrix probes, etc. The objective of this paper is to study the feasibility of CS for the reconstruction of channel RF data, i.e. the 2D set of raw RF lines gathered at the receive elements. Successful application of CS implies selecting a representation basis where the data to be reconstructed have a sparse expansion. Because they consist mainly in warped oscillatory patterns, channel RF data do not easily lend themselves to a sparse representation and thus represent a specific challenge. Within this perspective, we propose to perform and assess CS reconstruction of channel RF data using the recently introduced wave atoms [1] representation, which exhibit advantageous properties for sparsely representing such oscillatory patterns. Reconstructions obtained using wave atoms are compared with the reconstruction performed with two conventional representation bases, namely Fourier and Daubechies wavelets. The first experiment was conducted on simulated channel RF data acquired from a numerical cyst phantom. The quality of the reconstructions was quantified through the mean absolute error at varying subsampling rates by removing 50-90% of the original samples. The results obtained for channel RF data reconstruction yield error ranges of [0.6-3.0]×10(-2), [0.2-2.6]×10(-2), [0.1-1.5]×10(-2), for wavelets, Fourier and wave atoms respectively. The error ranges observed for the associated beamformed log-envelope images are [2.4-20.6]dB, [1.1-12.2]dB, and [0.5-8.8dB] using wavelets, Fourier, and wave atoms, respectively. These results thus show the superiority of the wave atom representation and the feasibility of CS for the reconstruction of US RF data. The second experiment aimed at showing the experimental feasibility of the method proposed using a data set acquired by imaging a general-purpose phantom (CIRS Model 054GS) using an Ultrasonix MDP scanner. The reconstruction was performed by removing 80% of the initial samples and using wave atoms. The reconstructed image was found to reliably preserve the speckle structure and was associated with an error of 5.5dB.

Robust alignment of prostate histology slices with quantified accuracy.

No current imaging technique is capable of detecting with precision tumors in the prostate. To evaluate each technique, the histology data must be precisely mapped to the imaged data. As the histology slices cannot be assumed to be cut along the same plane as the imaged data were acquired, the registration must be considered as a 3-D problem. This requires the prior alignment of the histology slices. We propose a protocol in which three needles are inserted into the fresh prostate, creating internal fiducial markers visible in the histology slices. Our algorithm then automatically detects and identifies these markers, enabling the automatic rigid alignment of each slice. The accuracy of the algorithm was quantified in simulated images, a beef liver sample in which a validation marker had been created, and ten prostate specimens. The simulated images showed that the algorithm has no associated residual error for a situation where there is no deformation. In the beef liver images, the average accuracy of the alignment was 0.12 ± 0.09 mm at the fiducial markers, and 0.62 ± 0.46 mm at a validation marker positioned approximately 20 mm from the fiducial markers. Concerning the ten prostates, there were 19.2 histology slices on average per specimen. On average, 93.7% of the fiducial markers created were visible in the slices, of which 96.1% were then automatically and correctly detected and identified, enabling an alignment of average accuracy 0.18 ± 0.13 mm at the fiducial markers. As a cancer of volume <0.5 cm(3) is classified as clinically insignificant, the accuracy achieved justified the choice of a rigid registration. An attractive feature of this method is the time required, less than 6 min on average per prostate specimen.

Embedding prior knowledge within compressed sensing by neural networks.

In the compressed sensing framework, different algorithms have been proposed for sparse signal recovery from an incomplete set of linear measurements. The most known can be classified into two categories: l(1) norm minimization-based algorithms and l(0) pseudo-norm minimization with greedy matching pursuit algorithms. In this paper, we propose a modified matching pursuit algorithm based on the orthogonal matching pursuit (OMP). The idea is to replace the correlation step of the OMP, with a neural network. Simulation results show that in the case of random sparse signal reconstruction, the proposed method performs as well as the OMP. Complexity overhead, for training and then integrating the network in the sparse signal recovery is thus not justified in this case. However, if the signal has an added structure, it is learned and incorporated in the proposed new OMP. We consider three structures: first, the sparse signal is positive, second the positions of the non zero coefficients of the sparse signal follow a certain spatial probability density function, the third case is a combination of both. Simulation results show that, for these signals of interest, the probability of exact recovery with our modified OMP increases significantly. Comparisons with l(1) based reconstructions are also performed. We thus present a framework to reconstruct sparse signals with added structure by embedding, through neural network training, additional knowledge to the decoding process in order to have better performance in the recovery of sparse signals of interest.

Generic remeshing of 3D triangular meshes with metric-dependent discrete voronoi diagrams.

In this paper, we propose a generic framework for 3D surface remeshing. Based on a metric-driven Discrete Voronoi Diagram construction, our output is an optimized 3D triangular mesh with a user defined vertex budget. Our approach can deal with a wide range of applications, from high quality mesh generation to shape approximation. By using appropriate metric constraints the method generates isotropic or anisotropic elements. Based on point-sampling, our algorithm combines the robustness and theoretical strength of Delaunay criteria with the efficiency of entirely discrete geometry processing . Besides the general described framework, we show experimental results using isotropic, quadric-enhanced isotropic and anisotropic metrics which prove the efficiency of our method on large meshes, for a low computational cost.

Fractal geometry of arterial coronary bifurcations: a quantitative coronary angiography and intravascular ultrasound analysis.

AIMS: Coronary artery bifurcations present a harmonious asymmetric geometry that is fractal in nature. Interventional treatment of bifurcation lesions is a major technical issue. The present study is aimed at a precise quantification of this geometry in the hope of deriving a formulation that would be simple to calculate. METHODS AND RESULTS: Forty seven patients with strictly normal coronarographic results obtained ahead of valve replacement were enrolled, and 27 of these underwent IVUS examination to confirm that their arteries were indeed normal. Three reference diameters were measured: those of the mother vessel (Dm) and of either daughter vessel (Dd1, Dd2). One hundred and seventy-three bifurcations were thus subjected to quantitative analysis. The mean diameter of the mother vessels was 3.33+/-0.94 mm, of the major daughter vessels 2.70+/-0.77 mm, and of the minor daughter vessels 2.23+/-0.68 mm. The ratio R=Dm/(Dd1+Dd2) of mother-vessel diameter to the sum of the two daughter-vessel diameters was 3.39/(2.708+2.236)=0.678. This ratio held at all levels of bifurcation: i.e., whatever diameter the mother vessel. CONCLUSION: The study confirmed the fractal nature of the geometry of the epicardial coronary artery tree, and gave a simple and accurate fractal ratio between the diameters of the mother and two daughter vessels such that Dm=0.678 (Dd1+Dd2). This makes it easy to calculate the precise diameter of any of the three vessels when those of the other two are known.

Motion correction for coronary stent reconstruction from rotational X-ray projection sequences.

This paper presents a new method for 3-D tomographic reconstruction of stent in X-ray cardiac rotational angiography. The method relies on 2-D motion correction from two radiopaque markerballs located on each side of the stent. The two markerballs are on a guidewire and linked to the balloon, which is introduced into the artery. Once the balloon has been inflated, deflated, and the stent deployed, a rotational sequence around the patient is acquired. Under the assumption that the guidewire and the stent have the same 3-D motion during rotational acquisition, we developed an algorithm to correct cardiac stent motion on the 2-D X-ray projection images. The 3-D image of the deployed stent is then reconstructed with the Feldkamp algorithm using all the available projections. Although the correction is an approximation, we show that the intrinsic geometrical error of our method has no visual impact on the reconstruction when the 2-D markerball centers are exactly detected and the markerballs have the same 3-D motion as the stent. Qualitative and quantitative results on simulated sequences under different realistic conditions demonstrate the robustness of the method. Finally, results from animal data acquired on a rotational angiography device are presented.

Compactly supported radial basis functions based collocation method for level-set evolution in image segmentation.

The partial differential equation driving level-set evolution in segmentation is usually solved using finite differences schemes. In this paper, we propose an alternative scheme based on radial basis functions (RBFs) collocation. This approach provides a continuous representation of both the implicit function and its zero level set. We show that compactly supported RBFs (CSRBFs) are particularly well suited to collocation in the framework of segmentation. In addition, CSRBFs allow us to reduce the computation cost using a kd-tree-based strategy for neighborhood representation. Moreover, we show that the usual reinitialization step of the level set may be avoided by simply constraining the l1-norm of the CSRBF parameters. As a consequence, the final solution is topologically more flexible, and may develop new contours (i.e., new zero-level components), which are difficult to obtain using reinitialization. The behavior of this approach is evaluated from numerical simulations and from medical data of various kinds, such as 3-D CT bone images and echocardiographic ultrasound images.

Wavelet-based progressive compression scheme for triangle meshes: wavemesh.

This paper proposes a new lossy to lossless progressive compression scheme for triangular meshes, based on a wavelet multiresolution theory for irregular 3D meshes. Although remeshing techniques obtain better compression ratios for geometric compression, this approach can be very effective when one wants to keep the connectivity and geometry of the processed mesh completely unchanged. The simplification is based on the solving of an inverse problem. Optimization of both the connectivity and geometry of the processed mesh improves the approximation quality and the compression ratio of the scheme at each resolution level. We show why this algorithm provides an efficient means of compression for both connectivity and geometry of 3D meshes and it is illustrated by experimental results on various sets of reference meshes, where our algorithm performs better than previously published approaches for both lossless and progressive compression.

Wavelet-based multiresolution analysis of irregular surface meshes.

This paper extends Lounsbery's multiresolution analysis wavelet-based theory for triangular 3D meshes, which can only be applied to regularly subdivided meshes and thus involves a remeshing of the existing 3D data. Based on a new irregular subdivision scheme, the proposed algorithm can be applied directly to irregular meshes, which can be very interesting when one wants to keep the connectivity and geometry of the processed mesh completely unchanged. This is very convenient in CAD (Computer-Assisted Design), when the mesh has attributes such as texture and color information, or when the 3D mesh is used for simulations, and where a different connectivity could lead to simulation errors. The algorithm faces an inverse problem for which a solution is proposed. For each level of resolution, the simplification is processed in order to keep the mesh as regular as possible. In addition, a geometric criterion is used to keep the geometry of the approximations as close as possible to the original mesh. Several examples on various reference meshes are shown to prove the efficiency of our proposal.

Nonseparable wavelet-based cone-beam reconstruction in 3-D rotational angiography.

In this paper, we propose a new wavelet-based reconstruction method suited to three-dimensional (3-D) cone-beam (CB) tomography. It is derived from the Feldkamp algorithm and is valid for the same geometrical conditions. The demonstration is done in the framework of nonseparable wavelets and requires ideally radial wavelets. The proposed inversion formula yields to a filtered backprojection algorithm but the filtering step is implemented using quincunx wavelet filters. The proposed algorithm reconstructs slice by slice both the wavelet and approximation coefficients of the 3-D image directly from the CB projection data. The validity of this multiresolution approach is demonstrated on simulations from both mathematical phantoms and 3-D rotational angiography clinical data. The same quality is achieved compared with the standard Feldkamp algorithm, but in addition, the multiresolution decomposition allows to apply directly image processing techniques in the wavelet domain during the inversion process. As an example, a fast low-resolution reconstruction of the 3-D arterial vessels with the progressive addition of details in a region of interest is demonstrated. Other promising applications are the improvement of image quality by denoising techniques and also the reduction of computing time using the space localization of wavelets.

Multiresolution reconstruction in fan-beam tomography.

In this paper, a new multiresolution reconstruction approach for fan-beam tomography is established. The theoretical development assumes radial wavelets. An approximate reconstruction formula based on a near-radial quincunx multiresolution scheme is proposed. This multiresolution algorithm allows to compute both the quincunx approximation and detail coefficients of an image from its fan-beam projections. Simulations on mathematical phantoms show that wavelet decomposition is acceptable for small beam angles but deteriorates at high angles. The main applications of the method are denoising and wavelet-based image analysis.