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STATISTICAL MORPHABLE MODEL FOR HUMAN TEETH RESTORATION

STATISTICAL MORPHABLE MODEL FOR HUMAN TEETH RESTORATION

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IEEE International Conference on image processing

作者： Eslam Mostafa Shireen Elhabian Aly Abdelrahim Salwa Elshazly Aly Farag Computer Vision and Image Processing Laboratory University of Louisville KY USA Kentucky Imaging Technologies Louisville KY USA

While traditional dental fillings are molded during a dental visit, dental restoration (e.g. inlays and onlays) are fabricated in a dental lab to offer a long lasting reparative solution to tooth decay or similar structural damage. Such process requires dental technicians who are highly trained experts in tooth anatomy to pick an appropriate standard tooth model from a tooth database. The success of a restoration process primarily relies on the acquisition and modeling of an accurate 3D shape of the occlusal surface of interest for manufacturing purposes. Based on a single optical image, this paper provides an economical and automated solution for tooth restoration where user intervention is kept at the minimal. The inherit relation between the photometric information and the underlying 3D shape is formulated as a coupled statistical model where the effect of illumination is modeled using Spherical Harmonics. Moreover, shape and texture alignment is accomplished using a proposed definition of anatomical jaw landmarks which are automatically detected. The system is evaluated on database of 32 jaws for crown, inlay, and onlay restoration. Results shows a promising performance for using the proposed approach in clinical application.

关键词： Teeth Shape Solid modeling Three-dimensional displays image restoration Dentistry Lighting

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Maximum likelihood thresholding algorithm based on four-parameter gamma distributions

Maximum likelihood thresholding algorithm based on four-para...

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International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Peter De-Ford Geovanni Martinez Image Processing and Computer Vision Research Laboratory (IPCV-LAB) Universidad de Costa Rica San José Costa Rica

In this contribution, we present a segmentation algorithm based on thresholding to subdivide an intensity image in the regions of object and background. The optimal threshold is found by maximizing a likelihood function derived from a novel intensity probability density function model, which consists of the sum of two weighted four-parameter gamma distributions, as a more flexible alternative to currently used models consisting of the sum of two weighted two-parameter Gaussian distributions. According to our experiments with 132 images, the proposed algorithm is in average slightly better than the best found in the scientific literature, performing particularly good in low contrast images. The additional parameters and complexity of its likelihood function resulted in an increase of the processing time by a factor of 3, from 0.003 sec/image to 0.009 sec/image.

关键词： image segmentation Probability density function Approximation algorithms Gaussian distribution Histograms Shape Pattern recognition

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A Novel Level Set Method for Segmentation of Left and Right Ventricles from Cardiac MR images

A Novel Level Set Method for Segmentation of Left and Right ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Yu Liu Chunming Li Shuxu Guo Yihua Song Yue Zhao Center for Image Processing and Computer Vision Jilin University Center for Biomedical Image Computing and Analytics University of Pennsylvania Center for Key Laboratory of Medical Image Computing Ministry of Education Northeastern University

ISBN: (纸本)9781424479276

In this paper, we propose a novel level set method for segmentation of cardiac left and right ventricles based on the distance regularized level set evolution (DRLSE) framework [7] and the distance regularized two-layer level set (DR2LS) model [17]. First, DRLSE is applied to obtain a preliminary segmentation of left and right ventricles, which is then used to initialize the endocardial contour, which is represented by the zero level contour of the level set function in our method. Then, the epicardial contour is represented by a different level contour of the same level set function. These two level sets are optimized by an energy minimization process to best fit the true endocardium and epicardium. In order to ensure smoothly varying distance between the two level contours, we introduce a distance regularization constraint in the energy function. With the region-scalable fitting (RSF) energy [8] as the data term, our method is able to deal with intensity inhomogeneities in the images, which is a main source of difficulty in image segmentation. Our method has been tested on cardiac MR images with promising results.

关键词： Level set image segmentation Nonhomogeneous media Biomedical imaging Educational institutions Myocardium

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Frameworks for multimodal biometric using sparse coding

Frameworks for multimodal biometric using sparse coding

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3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012

作者： Huang, Zengxi Liu, Yiguang Huang, Ronggang Yang, Menglong Vision and Image Processing Laboratory College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9783642366680

In this paper, we will introduce three frameworks for multimodal biometric using sparse representation based classification (SRC), which has been successfully used in many classification tasks recently. The first framework is multimodal SRC at match score level (MSRC-s), in which feature of each modality is sparsely coded independently, and then their representation fidelities are used as match scores for multimodal classification. The other two frameworks are multimodal SRC at feature level (MSRC-f1, MSRC-f2), where features of all modalities are first fused and then classified by using SRC. The difference between them is that MSRC-f1 fuses the features to form a unique multimodal feature vector, while MSRC-f2 implicitly combines the features in an iterative joint sparse coding process. As a typical application, the fusion of face and ear for human identification is investigated by using the three frameworks. In our experiments, Principal Component Analysis (PCA) based feature extraction is applied. Many results demonstrate that the proposed multimodal methods are significantly better than the multimodal recognition using common classifiers. Among the SRC based methods, MSRC-s gets the top recognition accuracy in almost all the test items, which might benefit from allowing sparse coding independence for different modalities. © Springer-Verlag 2013.

关键词： Principal component analysis

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Model-based human teeth shape recovery from a single optical image with unknown illumination

Model-based human teeth shape recovery from a single optical...

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15th International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Farag, Aly Elhabian, Shireen Abdelrehim, Aly Aboelmaaty, Wael Farman, Allan Tasman, David Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292 United States School of Dentistry University of Louisville Louisville KY 40202 United States

ISBN: (纸本)9783642366192

Several existing 3D systems for dental applications rely on obtaining an intermediate solid model of the jaw (cast or teeth imprints) from which the 3D information can be captured. In this paper, we propose a model-based shape-from-shading (SFS) approach which allows for the construction of plausible human jaw models in vivo, without ionizing radiation, using fewer sample points in order to reduce the cost and intrusiveness of acquiring models of patients teeth/jaws over time. The inherent relation between the photometric information and the underlying 3D shape is formulated as a statistical model where the effect of illumination is modeled using Spherical Harmonics (SH) and the partial least square (PLS) approach is deployed to carry out the estimation of dense 3D shapes. Moreover, shape and texture alignment is accomplished using a proposed definition of anatomical jaw landmarks which can be automatically detected. Vis-à-vis dental applications, the results demonstrate a significant increase in accuracy in favor of the proposed approach. In particular, our approach is able to recover geometrical details of tooth occlusal surface as well as mouth floor and ceiling as compared to SFS-based approaches. © 2013 Springer-Verlag.

关键词： Ionizing radiation

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Analytic Bilinear Appearance Subspace Construction for Modeling image Irradiance Under Natural Illumination and Non-Lambertian Reflectance

Analytic Bilinear Appearance Subspace Construction for Model...

引用

IEEE Conference on computer vision and Pattern Recognition

作者： Shireen Y. Elhabian Aly A. Farag Computer Vision and Image Processing Laboratory ECE Dept. University of Louisville

ISBN: (纸本)9781467364102

Conventional subspace construction approaches suffer from the need of "large-enough" image ensemble rendering numerical methods intractable. In this paper, we propose an analytic formulation for low-dimensional subspace construction in which shading cues lie while preserving the natural structure of an image sample. Using the frequency-space representation of the image irradiance equation, the process of finding such subspace is cast as establishing a relation between its principal components and that of a deterministic set of basis functions, termed as irradiance harmonics. Representing images as matrices further lessen the number of parameters to be estimated to define a bilinear projection which maps the image sample to a lower-dimensional bilinear subspace. Results show significant impact on dimensionality reduction with minimal loss of information as well as robustness against noise.

关键词： Lighting Harmonic analysis Principal component analysis image reconstruction Materials Gold Surface reconstruction

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Clinically desired segmentation method for vertebral bodies

Clinically desired segmentation method for vertebral bodies

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IEEE International Symposium on Biomedical Imaging

作者： Melih S. Aslan Ahmed Shalaby Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

ISBN: (纸本)9781467364560

In this paper, we propose a clinically desired segmentation method for vertebral bodies (VBs) in computed tomography (CT) images. Three pieces of information (intensity, spatial interaction, and shape) are modeled to optimize a new probabilistic energy functional; and hence to obtain the optimum segmentation. The information of the intensity and spatial interaction are modeled using the Gaussian and Gibbs distribution, respectively. A shape model is proposed using a new probabilistic function to enhance the segmentation results. This model is a generic shape information which is obtained using the cervical, lumbar, and thoracic spinal regions. We propose a semiautomated segmentation algorithm which uses limited interventions only in the VB separation process. The overall segmentation process completes the task in very low execution time which is one of the most important contribution of this paper. The proposed method is validated with clinical CT images and on a phantom with various Gaussian noise levels. This study reveals that the proposed method is robust under various noise levels, less variant to the initialization, and quite faster than alternative methods. One of the most important contributions of our paper is to offer a segmentation framework which can be suitable to the clinical works with acceptable results.

关键词： image segmentation Shape Computed tomography Probabilistic logic Object segmentation Level set Training

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Heat front propagation contours for 3D face recognition

Heat front propagation contours for 3D face recognition

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IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Mostafa Abdelrahman Aly A. Farag Moumen El-Melegy Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA University of Louisville Louisville KY US

Face recognition is a key biometric method aiming at identifying individuals by the features of face. Due to the challenges facing face recognition from 2D images, researchers have resorted to 3D face recognition. Our work in this paper is motivated by the recent and remarkable success of heat-based features for 3D object classification and retrieval. We propose an approach for 3D face recognition based on the front contours of heat propagation over the face surface. The front contours are extracted automatically as heat is propagating starting from a detected set of landmarks. The propagation contours are used to successfully discriminate the various faces. The proposed approach is evaluated on the largest publicly available database of 3D facial images and successfully compared to the state-of-the-art approaches in the literature.

关键词： Face Heating Three-dimensional displays Face recognition Mathematical model Kernel Equations

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DEFORMABLE MODELS FOR RANDOM SMALL-SIZE OBJECTS: CASE OF LUNG NODULES IN CT TOMOGRAPHY

DEFORMABLE MODELS FOR RANDOM SMALL-SIZE OBJECTS: CASE OF LUN...

引用

IEEE International Conference on Acoustics, Speech and Signal processing

作者： Amal A. Farag James H. Graham Aly A. Farag Imaging Biomarkers and Computer-Aided Diagnosis Laboratory National Institutes of Health Bethesda MD Computer Vision and Image Processing Laboratory University of Louisville Louisville KY

ISBN: (纸本)9781479903573

Deformable models are common in image modeling and analysis. Random objects provide major challenges as shapes and appearances are hard to quantify;hence, formulation of deformable models are much harder to construct and validate. In this paper, we examine the effect of randomness on building the shape and appearance models for small-size lung nodules (< 1cm) which appear in computed tomography (CT) of the human chest. We devise an approach for annotation, which lends a standard mechanism for building traditional active appearance (AAM), active shape (ASM) and active tensor models (ATM). We illustrate the effectiveness of AAM for nodule detection.

关键词： CT ASM AAM Army Achievement Medal X-Ray Computed Tomography Deformable models computer aided software engineering Packaging Case Root nodules Shape Lung nodules Tomography ANNOTATIONS

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SHREC'13 Track: Retrieval of objects captured with low-cost depth-sensing cameras 6

SHREC'13 Track: Retrieval of objects captured with low-cost ...

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6th Eurographics Workshop on 3D Object Retrieval, 3DOR 2013

作者： Machado, J. Ferreira, A. Pascoal, P.B. Abdelrahman, M. Aono, M. El-Melegy, M. Farag, A. Johan, H. Li, B. Lu, Y. Tatsuma, A. INESC-ID IST Technical University of Lisbon Portugal Microsoft Language Development Center Portugal Toyohashi University of Technology Japan Department of Computer Science Texas State University San Marcos United States Fraunhofer IDM at NTU Singapore Singapore Computer Vision and Image Processing Laboratory University of Louisville Louisville United States

ISBN: (纸本)9783905674446

The SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras is a first attempt at evaluating the effectiveness of 3D shape retrieval algorithms in low fidelity model databases, such as the ones captured with commodity depth cameras. Both target and query set are composed by objects captured with a Kinect camera and the objective is to retrieve the models in the target set who were considered relevant by a human-generated ground truth. Given how widespread such devices are, and how easy it is becoming for an everyday user to capture models in his household, the necessity of algorithms for these new types of 3D models is also increasing. Three groups have participated in the contest, providing rank lists for the set of queries, which is composed of 12 models from the target set. © The Eurographics Association 2013.

关键词： Cameras

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