Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 63, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2020.102241
Keywords
Image segmentation; Level sets; Prior knowledge; Pose invariance; Intrinsic alignment
Categories
Funding
- CONICYT FONDECYT Postdoctorado 2019 [3190763]
- CONICYT PCI REDES [180090]
- ANID - Millennium Science Initiative Program [NCN17_129, NCN17_059]
- CONICYT Fondecyt de Iniciacion [11160728]
- CONICYT FONDECYT [1180525, 1181057]
- ANID FONDECYT [1191710]
- ANID - PIA [ACT192064]
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This paper presents a new method for incorporating shape prior knowledge based on intrinsic alignment approach, but extending it for scaling, translation and rotation invariance. The approach uses a regularization term based on eigenvalues and eigenvectors of the covariance matrix, leading to a new set of evolution equations. Testing on 2D and 3D synthetic and medical images shows the effectiveness of using shape priors with intrinsic scaling, translation and rotation alignment in different segmentation problems.
Level set segmentation has been successfully used in several image applications. However, they perform poorly when applied to severely corrupted images or when the object's boundaries are blurred or occluded. Poor performance can be improved by introducing shape prior knowledge into the segmentation process by considering additional shape information from training examples. This can be achieved by adding a regularization term that penalizes shapes that differ from those learned from a training database. This regularizer must be invariant under translation, rotation and scaling transformations. Previous works have proposed coupling the curve evolution to a registration problem through an optimization procedure. This approach is slow and its results depend on how this optimization is implemented. An alternative approach introduced an intrinsic alignment, which normalizes each shape to be compared on a common coordinate system, avoiding the registration process. Nevertheless, the proposed intrinsic alignment considered only scaling and translation but not rotation, which is critical in several image applications. In this paper we present a new method to incorporate shape prior knowledge based on the intrinsic alignment approach, but extending it for scaling, translation and rotation invariance. Our approach uses a regularization term based on the eigenvalues and eigenvectors of the covariance matrix of each training shape, and this eigendecomposition dependency leads to a new set of evolution equations. We tested our regularizer, combined with Chan-Vese, in 2D and 3D synthetic and medical images, demonstrating the effectiveness of using shape priors with intrinsic scaling, translation and rotation alignment in different segmentation problems.
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