14 research outputs found
Linear Shape Deformation Models with Local Support Using Graph-based Structured Matrix Factorisation
Representing 3D shape deformations by linear models in high-dimensional space
has many applications in computer vision and medical imaging, such as
shape-based interpolation or segmentation. Commonly, using Principal Components
Analysis a low-dimensional (affine) subspace of the high-dimensional shape
space is determined. However, the resulting factors (the most dominant
eigenvectors of the covariance matrix) have global support, i.e. changing the
coefficient of a single factor deforms the entire shape. In this paper, a
method to obtain deformation factors with local support is presented. The
benefits of such models include better flexibility and interpretability as well
as the possibility of interactively deforming shapes locally. For that, based
on a well-grounded theoretical motivation, we formulate a matrix factorisation
problem employing sparsity and graph-based regularisation terms. We demonstrate
that for brain shapes our method outperforms the state of the art in local
support models with respect to generalisation ability and sparse shape
reconstruction, whereas for human body shapes our method gives more realistic
deformations.Comment: Please cite CVPR 2016 versio
Statistical shape model reconstruction with sparse anomalous deformations: application to intervertebral disc herniation
Many medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented. It combines the advantages of traditional statistical shape models (defining a ‘normal’ shape space) and previously presented sparse shape composition (providing localized descriptors of anomalies). The algorithm was incorporated into our image segmentation and classification software. Evaluation was performed on simulated and clinical MRI data from 22 sciatica patients with intervertebral disc herniation, containing 35 herniated and 97 normal discs. Moderate to high correlation (R = 0.73) was achieved between simulated and detected herniations. The sparse reconstruction provided novel quantitative features describing the herniation morphology and MRI signal appearance in three dimensions (3D). The proposed descriptors of local disc morphology resulted to the 3D segmentation accuracy of 1.07 ± 1.00 mm (mean absolute vertex-to-vertex mesh distance over the posterior disc region), and improved the intervertebral disc classification from 0.888 to 0.931 (area under receiver operating curve). The results show that the sparse shape reconstruction may improve computer-aided diagnosis of pathological conditions presenting local morphological alterations, as seen in intervertebral disc herniation
CoShaRP: A Convex Program for Single-shot Tomographic Shape Sensing
We introduce single-shot X-ray tomography that aims to estimate the target
image from a single cone-beam projection measurement. This linear inverse
problem is extremely under-determined since the measurements are far fewer than
the number of unknowns. Moreover, it is more challenging than conventional
tomography where a sufficiently large number of projection angles forms the
measurements, allowing for a simple inversion process. However, single-shot
tomography becomes less severe if the target image is only composed of known
shapes. Hence, the shape prior transforms a linear ill-posed image estimation
problem to a non-linear problem of estimating the roto-translations of the
shapes. In this paper, we circumvent the non-linearity by using a dictionary of
possible roto-translations of the shapes. We propose a convex program CoShaRP
to recover the dictionary-coefficients successfully. CoShaRP relies on
simplex-type constraint and can be solved quickly using a primal-dual
algorithm. The numerical experiments show that CoShaRP recovers shapes stably
from moderately noisy measurements.Comment: Paper is currently under consideration for Pattern Recognition
Letter