16,217 research outputs found
Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation
This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm.
We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images.
We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy
3D reconstruction of ribcage geometry from biplanar radiographs using a statistical parametric model approach
Rib cage 3D reconstruction is an important prerequisite for thoracic spine modelling, particularly for studies of the deformed thorax in adolescent idiopathic scoliosis. This study proposes a new method for rib cage 3D reconstruction from biplanar radiographs, using a statistical parametric model approach. Simplified parametric models were defined at the hierarchical levels of rib cage surface, rib midline and rib surface, and applied on a database of 86 trunks. The resulting parameter database served to statistical models learning which were used to quickly provide a first estimate of the reconstruction from identifications on both radiographs. This solution was then refined by manual adjustments in order to improve the matching between model and image. Accuracy was assessed by comparison with 29 rib cages from CT scans in terms of geometrical parameter differences and in terms of line-to-line error distance between the rib midlines. Intra and inter-observer reproducibility were determined regarding 20 scoliotic patients. The first estimate (mean reconstruction time of 2’30) was sufficient to extract the main rib cage global parameters with a 95% confidence interval lower than 7%, 8%, 2% and 4° for rib cage volume, antero-posterior and lateral maximal diameters and maximal rib hump, respectively. The mean error distance was 5.4 mm (max 35mm) down to 3.6 mm (max 24 mm) after the manual adjustment step (+3’30). The proposed method will improve developments of rib cage finite element modeling and evaluation of clinical outcomes.This work was funded by Paris Tech BiomecAM chair on subject specific muscular skeletal modeling, and we express our acknowledgments to the chair founders: Cotrel foundation, Société générale, Protéor Company and COVEA consortium. We extend your acknowledgements to Alina Badina for medical imaging data, Alexandre Journé for his advices, and Thomas Joubert for his technical support
Image and Volume Segmentation by Water Flow
A general framework for image segmentation is presented in this paper, based on the paradigm of water flow. The major water flow attributes like water pressure, surface tension and capillary force are defined in the context of force field generation and make the model adaptable to topological and geometrical changes. A flow-stopping image functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method is assessed qualitatively and quantitatively on synthetic and natural images. It is shown that the new approach can segment objects with complex shapes or weak-contrasted boundaries, and has good immunity to noise. The operator is also extended to 3-D, and is successfully applied to medical volume segmentation
Medical Image Segmentation by Water Flow
We present a new image segmentation technique based on the paradigm of water flow and apply it to medical images. The force field analogy is used to implement the major water flow attributes like water pressure, surface tension and adhesion so that the model achieves topological adaptability and geometrical flexibility. A new snake-like force functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method has been assessed qualitatively and quantitatively, and shows decent detection performance as well as ability to handle noise
Nonlinear tube-fitting for the analysis of anatomical and functional structures
We are concerned with the estimation of the exterior surface and interior
summaries of tube-shaped anatomical structures. This interest is motivated by
two distinct scientific goals, one dealing with the distribution of HIV
microbicide in the colon and the other with measuring degradation in
white-matter tracts in the brain. Our problem is posed as the estimation of the
support of a distribution in three dimensions from a sample from that
distribution, possibly measured with error. We propose a novel tube-fitting
algorithm to construct such estimators. Further, we conduct a simulation study
to aid in the choice of a key parameter of the algorithm, and we test our
algorithm with validation study tailored to the motivating data sets. Finally,
we apply the tube-fitting algorithm to a colon image produced by single photon
emission computed tomography (SPECT) and to a white-matter tract image produced
using diffusion tensor imaging (DTI).Comment: Published in at http://dx.doi.org/10.1214/10-AOAS384 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors
This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
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