5,821 research outputs found
Estimation of vector fields in unconstrained and inequality constrained variational problems for segmentation and registration
Vector fields arise in many problems of computer vision, particularly in non-rigid registration. In this paper, we develop coupled partial differential equations (PDEs) to estimate vector fields that define the deformation between
objects, and the contour or surface that defines the segmentation of the objects as well.We also explore the utility of inequality constraints applied to variational problems in vision such as estimation of deformation fields in non-rigid registration and tracking. To solve inequality constrained vector
field estimation problems, we apply tools from the Kuhn-Tucker theorem in optimization theory. Our technique differs from recently popular joint segmentation and registration algorithms, particularly in its coupled set of PDEs derived from the same set of energy terms for registration and
segmentation. We present both the theory and results that demonstrate our approach
Comparative anchorage maintenance between the intercanine coil, lip bumper, and mandibular cervical traction during cuspid retraction
Thesis (M.Sc.D.)--Boston University School of Graduate Dentistry, 1972 (Orthodontics)Bibliography included.The present study was undertaken to compare the efficiency of three different biomechanical mechanisms in preserving mandibular molar anchorage.
Thirty-five patients were treated with intercanine coil, lip
bumper, 9r mandibular cervical traction through the end of cuspid
retraction. Midtreatment cephalograms were then taken. Superimposition
of these midtreatment cepbalograms with the pretreatment
cephalograms provided the author with the net mesial or distal
movement of the mandibular first molar in each case. The data
obtained from each case was accordingly placed in the appropriate
biomechanical group. Each group was then statistically related to
one another by means of the Mann-Whitney U Test. It was found that
a stastically significant difference existed between lower cervical
traction and the intercanine coil. The confidence level
obtained (P < .02) indicated that less than two cases out of a
hundred had a chance of coming from the same population.
This data also showed a mean increase in mandibular anchorage
with lower cervical traction ( +.062 mm. gained) indicating that there may be very good possibilities for this system to be used in orthodontic cases when anchorage is of a critical nature
Compressed Sensing Based Reconstruction Algorithm for X-ray Dose Reduction in Synchrotron Source Micro Computed Tomography
Synchrotron computed tomography requires a large number of angular projections to reconstruct tomographic images with high resolution for detailed and accurate diagnosis. However, this exposes the specimen to a large amount of x-ray radiation. Furthermore, this increases scan time and, consequently, the likelihood of involuntary specimen movements. One approach for decreasing the total scan time and radiation dose is to reduce the number of projection views needed to reconstruct the images. However, the aliasing artifacts appearing in the image due to the reduced number of projection data, visibly degrade the image quality. According to the compressed sensing theory, a signal can be accurately reconstructed from highly undersampled data by solving an optimization problem, provided that the signal can be sparsely represented in a predefined transform domain. Therefore, this thesis is mainly concerned with designing compressed sensing-based reconstruction algorithms to suppress aliasing artifacts while preserving spatial resolution in the resulting reconstructed image. First, the reduced-view synchrotron computed tomography reconstruction is formulated as a total variation regularized compressed sensing problem. The Douglas-Rachford Splitting and the randomized Kaczmarz methods are utilized to solve the optimization problem of the compressed sensing formulation.
In contrast with the first part, where consistent simulated projection data are generated for image reconstruction, the reduced-view inconsistent real ex-vivo synchrotron absorption contrast micro computed tomography bone data are used in the second part. A gradient regularized compressed sensing problem is formulated, and the Douglas-Rachford Splitting and the preconditioned conjugate gradient methods are utilized to solve the optimization problem of the compressed sensing formulation. The wavelet image denoising algorithm is used as the post-processing algorithm to attenuate the unwanted staircase artifact generated by the reconstruction algorithm.
Finally, a noisy and highly reduced-view inconsistent real in-vivo synchrotron phase-contrast computed tomography bone data are used for image reconstruction. A combination of prior image constrained compressed sensing framework, and the wavelet regularization is formulated, and the Douglas-Rachford Splitting and the preconditioned conjugate gradient methods are utilized to solve the optimization problem of the compressed sensing formulation. The prior image constrained compressed sensing framework takes advantage of the prior image to promote the sparsity of the target image. It may lead to an unwanted staircase artifact when applied to noisy and texture images, so the wavelet regularization is used to attenuate the unwanted staircase artifact generated by the prior image constrained compressed sensing reconstruction algorithm.
The visual and quantitative performance assessments with the reduced-view simulated and real computed tomography data from canine prostate tissue, rat forelimb, and femoral cortical bone samples, show that the proposed algorithms have fewer artifacts and reconstruction errors than other conventional reconstruction algorithms at the same x-ray dose
An Optimal Region Of Interest Localization Using Edge Refinement Filter And Entropy-Based Measurement For Point Spread Function Stimation
The use of edges to determine an optimal region of interest (ROI) location is
increasingly becoming popular for image deblurring. Recent studies have shown that
regions with strong edges tend to produce better deblurring results. In this study, a
direct method for ROI localization based on edge refinement filter and entropy-based
measurement is proposed. Using this method, the randomness of grey level distribution
is quantitatively measured, from which the ROI is determined. This method has low
computation cost since it contains no matrix operations. The proposed method has
been tested using three sets of test images - Dataset I, II and III. Empirical results
suggest that the improved edge refinement filter is competitive when compared to the
established edge detection schemes and achieves better performance in the Pratt's
figure-of-merit (PFoM) and the twofold consensus ground truth (TCGT); averaging at
15.7 % and 28.7 %, respectively. The novelty of the proposed approach lies in the use
of this improved filtering strategy for accurate estimation of point spread function
(PSF), and hence, a more precise image restoration. As a result, the proposed solutions
compare favourably against existing techniques with the peak signal-to-noise ratio
(PSNR), kernel similarity (KS) index, and error ratio (ER) averaging at 24.8 dB, 0.6
and 1.4, respectively. Additional experiments involving real blurred images
demonstrated the competitiveness of the proposed approach in performing restoration
in the absent of PSF
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