15,701 research outputs found
Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications
In this paper we present a semi-automatic 2D-3D local registration pipeline
capable of coloring 3D models obtained from 3D scanners by using uncalibrated
images. The proposed pipeline exploits the Structure from Motion (SfM)
technique in order to reconstruct a sparse representation of the 3D object and
obtain the camera parameters from image feature matches. We then coarsely
register the reconstructed 3D model to the scanned one through the Scale
Iterative Closest Point (SICP) algorithm. SICP provides the global scale,
rotation and translation parameters, using minimal manual user intervention. In
the final processing stage, a local registration refinement algorithm optimizes
the color projection of the aligned photos on the 3D object removing the
blurring/ghosting artefacts introduced due to small inaccuracies during the
registration. The proposed pipeline is capable of handling real world cases
with a range of characteristics from objects with low level geometric features
to complex ones
A fast and robust patient specific Finite Element mesh registration technique: application to 60 clinical cases
Finite Element mesh generation remains an important issue for patient
specific biomechanical modeling. While some techniques make automatic mesh
generation possible, in most cases, manual mesh generation is preferred for
better control over the sub-domain representation, element type, layout and
refinement that it provides. Yet, this option is time consuming and not suited
for intraoperative situations where model generation and computation time is
critical. To overcome this problem we propose a fast and automatic mesh
generation technique based on the elastic registration of a generic mesh to the
specific target organ in conjunction with element regularity and quality
correction. This Mesh-Match-and-Repair (MMRep) approach combines control over
the mesh structure along with fast and robust meshing capabilities, even in
situations where only partial organ geometry is available. The technique was
successfully tested on a database of 5 pre-operatively acquired complete femora
CT scans, 5 femoral heads partially digitized at intraoperative stage, and 50
CT volumes of patients' heads. The MMRep algorithm succeeded in all 60 cases,
yielding for each patient a hex-dominant, Atlas based, Finite Element mesh with
submillimetric surface representation accuracy, directly exploitable within a
commercial FE software
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Algorithms to automatically quantify the geometric similarity of anatomical surfaces
We describe new approaches for distances between pairs of 2-dimensional
surfaces (embedded in 3-dimensional space) that use local structures and global
information contained in inter-structure geometric relationships. We present
algorithms to automatically determine these distances as well as geometric
correspondences. This is motivated by the aspiration of students of natural
science to understand the continuity of form that unites the diversity of life.
At present, scientists using physical traits to study evolutionary
relationships among living and extinct animals analyze data extracted from
carefully defined anatomical correspondence points (landmarks). Identifying and
recording these landmarks is time consuming and can be done accurately only by
trained morphologists. This renders these studies inaccessible to
non-morphologists, and causes phenomics to lag behind genomics in elucidating
evolutionary patterns. Unlike other algorithms presented for morphological
correspondences our approach does not require any preliminary marking of
special features or landmarks by the user. It also differs from other seminal
work in computational geometry in that our algorithms are polynomial in nature
and thus faster, making pairwise comparisons feasible for significantly larger
numbers of digitized surfaces. We illustrate our approach using three datasets
representing teeth and different bones of primates and humans, and show that it
leads to highly accurate results.Comment: Changes with respect to v1, v2: an Erratum was added, correcting the
references for one of the three datasets. Note that the datasets and code for
this paper can be obtained from the Data Conservancy (see Download column on
v1, v2
Synthetic aperture radar/LANDSAT MSS image registration
Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint
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