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Novel surface registration using the grid closest point (GCP) transform

IEEE International Conference on image processing

作者： S.M. Yamany M.N. Ahmed E.E. Hemayed A.A. Farag Computer Vision and Image Processing Laboratory Electrical Engineering Department University of Louisville Louisville KY USA

ISBN: (纸本)0818688211

A novel approach has been developed for fast registration of two sets of 3-D curves or surfaces. The technique is an extension of Besl and Mackay's (1992) iterative closest point (ICP) algorithm. This technique solves the computational complexity associated with the ICP algorithm by applying a novel grid closest point (GCP) transform and a genetic algorithm to minimize the cost function. A detailed description of the algorithm is presented along with a comparison of its performance versus several registration techniques. Two applications are presented in this paper. In the first, the algorithm is used to register 2-D head contours extracted from CT/MRI data to correct for possible mis-alignment caused by motion artifacts during scanning. In the second, the algorithm is used to register 3-D segments of the human jaw obtained using the shape from shading technique. Registration using the GCP/GA technique is found to be significantly faster and of comparable accuracy than two popular techniques in the computer vision and medical imaging literature.

关键词： Iterative algorithms Iterative closest point algorithm Computational complexity Genetic algorithms Cost function Head Data mining Computed tomography Magnetic resonance imaging Registers

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A system for human jaw modeling using intra-oral images

A system for human jaw modeling using intra-oral images

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： S.M. Yamany A.A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

A novel integrated system is developed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video camera. A modified Shape from Shading (SFS) technique using perspective projection and camera calibration is then used to extract accurate 3D information from a sequence of 2D images of the jaw. A novel technique for 3D data registration using Grid Closest Point (GCP) transform and genetic algorithms (GA) is used to register the output of the SFS stage. Triangulization is then performed, and a solid 3D model is obtained via a rapid prototype machine. The overall purpose of this research is to develop a model-based vision system for orthodontics that will replace traditional approaches and can be used in diagnosis, treatment planning, surgical simulation and implant purposes.

关键词： Humans Cameras computer vision Data acquisition Shape Calibration Data mining Genetic algorithms Solid modeling Prototypes

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Active contours: an overview with applications to motion artifact cancellation in MRI

Active contours: an overview with applications to motion art...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： D.L. Guidry A.A. Farag Computer Vision and Image Processing Lab Electrical Engineering Department University of Louisville Louisville KY USA

Motion can be estimated by detecting the edges of a moving object using Active Contours, and registering them together to obtain the motion model parameters. This idea can be applied to patient motion during the acquisition of an MRI to eliminate motion artifacts in the image. The data obtained during the MRI acquistion, the k-space, can be divided into several subbands such that each subband is acquired in a small fraction of the full imaging time. These subbands create invariant tissue feature maps called subband images. Using Active Contours, the relative motion is analyzed across the different subband images to determine the motion parameters. Using these motion parameters it is possible to correct the subbands, thus correcting the k-space. This has the potential to yield clear, noise-free MR images.

关键词： Active contours Magnetic resonance imaging image edge detection computer vision Motion estimation Object detection Motion detection Motion analysis Gray-scale Application software

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1.5V resistive fuse for image smoothing and segmentation

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ELECTRONICS LETTERS 1997年第10期33卷 851-852页

作者： Athreya, JKV Gregson, PH Department of Electrical Engineering Computer Vision and Image Processing Laboratory Technical University of Nova Scotia Halifax Canada

A 1.5V resistive fuse for image smoothing and segmentation using bulk-driven MOSFETs is presented. The circuit switches on only if the differential voltage applied across its input terminals is less than a set voltage;it switches off if the differential voltage is higher than the set value. The useful operation range of the circuit is 0.4V with a supply voltage of 1.5V and threshold voltages of V-Tn = 0.828V and V-Tp = -0.56V for n and g channel MOSFETs, respectively.

关键词： image processing segmentation (image processing)

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Two-stage neural network for volume segmentation of medical images

Two-stage neural network for volume segmentation of medical ...

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1997 IEEE International Conference on Neural Networks (ICNN 97)

作者： Ahmed, MN Farag, AA Computer Vision and Image Processing Laboratory University of Louisville Department of Electrical Engineering Louisville KY 40292 United States

ISBN: (纸本)0780341236

In this paper, we present a new system to segment and label CT/MRI Brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be apdated to real-time imaging scenarios.

关键词： segmentation pattern analysis learning in vision

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3D reconstruction of the human jaw from a sequence of images

3D reconstruction of the human jaw from a sequence of images

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M. Ahmed S. Yamany E. Hemayed S. Ahmed S. Roberts A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.

关键词： image reconstruction Humans computer vision Data acquisition Cameras Shape Data mining Genetic algorithms Implants Machine vision

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Two-stage neural network for volume segmentation of medical images

Two-stage neural network for volume segmentation of medical ...

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International Conference on Neural Networks

作者： M.N. Ahmed A.A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

We present a system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be adapted to real-time imaging scenarios.

关键词： Neural networks image segmentation Biomedical imaging Feature extraction Principal component analysis Computed tomography Magnetic resonance imaging Brain Labeling Clustering algorithms

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A spatialoral parallel approach for real-time MPEG video compression 25

A spatialoral parallel approach for real-time MPEG video com...

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25th International Conference on Parallel processing, ICPP 1996

作者： Shen, Ke Delp, E.J. Purdue University School of Electrical Engineering Computer Vision and Image Processing Laboratory West LafayetteIN United States Purdue Multimedia Testbed West LafayetteIN47907-1285 United States

ISBN: (纸本)081867623X

We present a parallel implementation of an MPEG encoder on the Intel Paragon supercomputer. In our approach, both spatial and temporal parallelism have been exploited, While the Paragon has the computation capacity to encode video sequences faster than real-time, we found that real-time performance cannot be achieved if the input/output techniques are not designed properly. We present several schemes for managing the I/O and show their effect on reducing I/O contention. © 1996 IEEE.

关键词： Supercomputers

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The EM/MPM algorithm for segmentation of textured images: analysis and further experimental results

The EM/MPM algorithm for segmentation of textured images: an...

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IEEE International Conference on image processing

作者： M.L. Comer E.J. Delp Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

In this paper we present new results relative to the "expectation-maximization/maximization of the posterior marginals" (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels. We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters. We also present new experimental results demonstrating the performance of the algorithm.

关键词： image segmentation image analysis image texture analysis Algorithm design and analysis Parameter estimation Pixel Stochastic processes computer vision image processing

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3D segmentation and labeling using self-organizing Kohonen network for volumetric measurements on brain CT imaging to quantify TBI recovery

3D segmentation and labeling using self-organizing Kohonen n...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： M.N. Ahmed A.A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses. Segmentation is performed by assigning a feature pattern to each voxel, consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is input to Kohonen network for an unsupervised classification of the voxels into regions.

关键词： Labeling Volume measurement image segmentation Computed tomography Layout Electric variables measurement engineering in Medicine and Biology Society Brain injuries Data mining Filters

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