M. Desvignes - Academia.edu (original) (raw)
Papers by M. Desvignes
Applications in Practice and Research, 2011
In this paper, we present a computer-assisted method for facial reconstruction : this method prov... more In this paper, we present a computer-assisted method for facial reconstruction : this method provides an estimation of the facial outlook associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of points extracted form the bone and soft-tissue surfaces. Facial reconstruction then attempt to predict the position of the soft-tissue surface points knowing the positions of the bone surface points. We propose to use linear latent variable regression methods for the prediction (such as Principal Component Regression or Latent Root Root Regression) and to compare the results obtained to those given by the use of statistical shape models. In conjunction, we have evaluated the influence of the number of skull landmarks used. Anatomical skull landmarks are completed iteratively by points located upon geodesics linking the anatomical landmarks. They enable us to artificially augment the number of skull points. Facial landmarks are obtained using a meshmatching algorithm between a common reference mesh and the individual soft-tissue surface meshes. The proposed method is validated in terms of accuracy, based on a leave-one-out crossvalidation test applied on a homogeneous database. Accuracy measures are obtained by computing the distance between the reconstruction and the ground truth. Finally, these results are discussed in regard to current computer-assisted facial reconstruction techniques, including deformation based techniques.
: Our framework enables visualization and processing of large medical images on modest computers.... more : Our framework enables visualization and processing of large medical images on modest computers. Here, a simple example of lung segmentation is shown.
ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., 2005
The Sixth IEEE International Conference on Computer and Information Technology (CIT'06), 2006
In forensic science, 3D cranio facail reconstruction is used to reconstruct the face from a skull... more In forensic science, 3D cranio facail reconstruction is used to reconstruct the face from a skull. This can be done by manual approaches or computer assisted methods. The proposed statistical model represents the relationship between the skull and the soft tissues and is inverted to reconstruct the unknown face from the known skull. It is a specific application of the missing or occulted data problem. Results are visually correct.
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000
We address the problem of locating some anatomical bone structures on lateral cranial X-Ray Image... more We address the problem of locating some anatomical bone structures on lateral cranial X-Ray Images. These structures are landmarks used in orthodontic therapy. The main problem in this pattern recognition application is that the landmarks are difficult to distinguish on images even for the human expert, because of lateral projection of the X-Ray process. We propose a 3 steps approach: the first step provides a statistical estimation of the landmarks, using an adaptative coordinates space. The second step computes a region of interest around the estimated landmark. In the third step, each landmark is precisely located using its anatomical definition. This paper describes the two first generic steps, and gives examples of the last step for two anatomical points. Intensity 1-3 pixels Curvilinear abscissa Figure 4 : Intensity model
Journal of Computing and Information Technology, 2006
The aim of craniofacial reconstruction is to estimate the shape of a face from the shape of the s... more The aim of craniofacial reconstruction is to estimate the shape of a face from the shape of the skull. Few works in machine-assisted facial reconstruction have been conducted so far, probably due to technical (poor machine performance and data availability) and theoretical (complexity) reasons. Therefore, the main works in the literature consist in manual reconstructions. In this paper, an original approach is first proposed to build a 3D statistical model of the skull/face set from 3D CT scans. Then, a reconstruction method is introduced in order to estimate, from this statistical model, the 3D facial shape of one subject from known skull data.
IEEE Transactions on Image Processing, 2000
We derive shortest-path constraints from graph models of structure adjacency relations and introd... more 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.
We propose an application of multi-label "Graph Cut" optimization algorithms to the simultaneous ... more We propose an application of multi-label "Graph Cut" optimization algorithms to the simultaneous segmentation of multiple anatomical structures, initialized via an oversegmentation of the image computed by a fast centroidal Voronoi diagram (CVD) clustering algorithm. With respect to comparable segmentations computed directly on the voxels of image volumes, we demonstrate performance improvements on both execution speed and memory footprint by, at least, an order of magnitude, making it possible to process large volumes on commodity hardware which could not be processed pixel-wise.
Applications in Practice and Research, 2011
In this paper, we present a computer-assisted method for facial reconstruction : this method prov... more In this paper, we present a computer-assisted method for facial reconstruction : this method provides an estimation of the facial outlook associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of points extracted form the bone and soft-tissue surfaces. Facial reconstruction then attempt to predict the position of the soft-tissue surface points knowing the positions of the bone surface points. We propose to use linear latent variable regression methods for the prediction (such as Principal Component Regression or Latent Root Root Regression) and to compare the results obtained to those given by the use of statistical shape models. In conjunction, we have evaluated the influence of the number of skull landmarks used. Anatomical skull landmarks are completed iteratively by points located upon geodesics linking the anatomical landmarks. They enable us to artificially augment the number of skull points. Facial landmarks are obtained using a meshmatching algorithm between a common reference mesh and the individual soft-tissue surface meshes. The proposed method is validated in terms of accuracy, based on a leave-one-out crossvalidation test applied on a homogeneous database. Accuracy measures are obtained by computing the distance between the reconstruction and the ground truth. Finally, these results are discussed in regard to current computer-assisted facial reconstruction techniques, including deformation based techniques.
: Our framework enables visualization and processing of large medical images on modest computers.... more : Our framework enables visualization and processing of large medical images on modest computers. Here, a simple example of lung segmentation is shown.
ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., 2005
The Sixth IEEE International Conference on Computer and Information Technology (CIT'06), 2006
In forensic science, 3D cranio facail reconstruction is used to reconstruct the face from a skull... more In forensic science, 3D cranio facail reconstruction is used to reconstruct the face from a skull. This can be done by manual approaches or computer assisted methods. The proposed statistical model represents the relationship between the skull and the soft tissues and is inverted to reconstruct the unknown face from the known skull. It is a specific application of the missing or occulted data problem. Results are visually correct.
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000
We address the problem of locating some anatomical bone structures on lateral cranial X-Ray Image... more We address the problem of locating some anatomical bone structures on lateral cranial X-Ray Images. These structures are landmarks used in orthodontic therapy. The main problem in this pattern recognition application is that the landmarks are difficult to distinguish on images even for the human expert, because of lateral projection of the X-Ray process. We propose a 3 steps approach: the first step provides a statistical estimation of the landmarks, using an adaptative coordinates space. The second step computes a region of interest around the estimated landmark. In the third step, each landmark is precisely located using its anatomical definition. This paper describes the two first generic steps, and gives examples of the last step for two anatomical points. Intensity 1-3 pixels Curvilinear abscissa Figure 4 : Intensity model
Journal of Computing and Information Technology, 2006
The aim of craniofacial reconstruction is to estimate the shape of a face from the shape of the s... more The aim of craniofacial reconstruction is to estimate the shape of a face from the shape of the skull. Few works in machine-assisted facial reconstruction have been conducted so far, probably due to technical (poor machine performance and data availability) and theoretical (complexity) reasons. Therefore, the main works in the literature consist in manual reconstructions. In this paper, an original approach is first proposed to build a 3D statistical model of the skull/face set from 3D CT scans. Then, a reconstruction method is introduced in order to estimate, from this statistical model, the 3D facial shape of one subject from known skull data.
IEEE Transactions on Image Processing, 2000
We derive shortest-path constraints from graph models of structure adjacency relations and introd... more 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.
We propose an application of multi-label "Graph Cut" optimization algorithms to the simultaneous ... more We propose an application of multi-label "Graph Cut" optimization algorithms to the simultaneous segmentation of multiple anatomical structures, initialized via an oversegmentation of the image computed by a fast centroidal Voronoi diagram (CVD) clustering algorithm. With respect to comparable segmentations computed directly on the voxels of image volumes, we demonstrate performance improvements on both execution speed and memory footprint by, at least, an order of magnitude, making it possible to process large volumes on commodity hardware which could not be processed pixel-wise.