Manual segmentation of pre-operative volumetric dataset is generally time consuming and results are subject
to large inter-user variabilities. Level-set methods have been proposed to improve segmentation consistency by
finding interactively the segmentation boundaries with respect to some priors. However, in thin and elongated
structures, such as major aorto-pulmonary collateral arteries (MAPCAs), edge-based level set methods might be
subject to flooding whereas region-based level set methods may not be selective enough. The main contribution
of this work is to propose a novel expert-guided technique for the segmentation of the aorta and of the attached
MAPCAs that is resilient to flooding while keeping the localization properties of an edge-based level set method.
In practice, a two stages approach is used. First, the aorta is delineated by using manually inserted seed points
at key locations and an automatic segmentation algorithm. The latter includes an intensity likelihood term that
prevents leakage of the contour in regions of weak image gradients. Second, the origins of the MAPCAs are
identified by using another set of seed points, then the MAPCAs' segmentation boundaries are evolved while
being constrained by the aorta segmentation. This prevents the aorta to interfere with the segmentation of the
MAPCAs. Our preliminary results are promising and constitute an indication that an accurate segmentation of
the aorta and MAPCAs can be obtained with reasonable amount of effort.
We propose a new fast stereoradiographic 3D reconstruction method for the spine. User input is limited to few points passing through the spine on two radiographs and two line segments representing the end plates of the limiting vertebrae. A 3D spline that hints the positions of the vertebrae in space is then generated. We then use wavelet multi-scale analysis (WMSA) to automatically localize specific features in both lateral and frontal radiographs. The WMSA gives an elegant spectral investigation that leads to gradient generation and edge extraction. Analysis of the information contained at several scales leads to the detection of 1) two curves enclosing the vertebral bodies' walls and 2) inter-vertebral spaces along the spine. From this data, we extract four points per vertebra per view, corresponding to the corners of the vertebral bodies. These points delimit a hexahedron in space where we can match the vertebral body. This hexahedron is then passed through a 3D statistical database built using local and global information generated from a bank of normal and scoliotic spines. Finally, models of the vertebrae are positioned with respect to these landmarks, completing the 3D reconstruction.
During the last decade, the optics community has shown interest in building bridges between mathematical wavelets and optical phenomena. In a first time, we review some of the previous works done on the subject. Namely, we discuss the optical implementation of the transform, as well as its utilization in relation with optical pattern matching. A short discussion on works, unfortunately falling short to explain scalar diffraction in terms of a wavelet transform, is presented. At this point, we introduce the physical wavelet (Psi) . After portraying the mathematical properties of (Psi) , we describe its contributions to the optical world. Actually, this wavelet being a solution of Maxwell's equations, we derive interesting optical properties from its mathematical behavior. For instance, looking more closely to the scalar projection of this wavelet, we demonstrate the equivalence between Huygens' diffraction principle and the wavelet transform using y as the transformation kernel. Another application involves a closely related form of this wavelet that can be used to generate limited diffraction beams.
We wavelet transform a non-diffracting field into a linear combination of the non-diffracting electromagnetic wavelets, which are spatially localized in the lateral direction, translated in the propagation direction and scaled in the time and temporal frequency. The non-diffracting wavelet is the window Fourier transform of the Bessel beam with a dilated temporal window. The proposed transform will be useful for analyzing the non-diffracting pulses and polychromatic beams.
We use the cubic spline interpolation instead of the linear interpolation of the feature space trajectories for 3-D object recognition from its 2-D perspective views. The accuracy of the interpolated trajectories to the testing views is greatly enhanced by this change. The experiments are on the real-world IR images of military vehicles. Edge-based approach is used to reduce the effect of the changes in infrared image intensity distribution due to temperature changes in some running parts of vehicles.
We show 3D object classification from their 2D infrared images. Our feature-based approach is robust to image brightness variations due to the temperature changes. An original feature space cubic spline interpolation is introduced to build feature space trajectories for the recognition.
We review the optical wavelets proposed by Onural and the electromagnetic wavelets proposed by Kaiser. We show that the wavelet transform of the electromagnetic wavelets can be computed with the direct inner product in the space time between the field and the scaled and shifted wavelets.In the case of monochromatic field, Kaiser's physical wavelets become monochromatic spherical wavelets.
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