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3D path planning for virtual endoscopy

International Congress Series 2005年 1281卷 115-120页

作者： Hassouna, M. Sabry Farag, A.A. Hushek, Stephen G. Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY 40292 United States iMRI Department Norton Hospital Louisville KY 40202 United States

Centerline extraction of curvilinear objects is a crucial component of virtual endoscopy (VE) because it provides path planning for automatic navigation. In this paper, we present a guided voxel coding (GVC) algorithm that modifies the original method to extract reliable centerlines suitable for fly-through applications. Our proposed method extracts potential centerline voxels with maximal spheres inscribed in the object and stores them in an a priori guidance map. Centerlines are initially extracted as trajectories inside the object and then centered using a new mechanism that exploits the information contained in that map. A new level set method accurately identifies the topological branching nodes of the joint centerlines. Finally, a thinning method is proposed to guarantee single voxel width centerlines. The proposed algorithm is completely automated and generates reliable, connected, and smoothed navigation paths for complex tree structures. © 2005.

关键词： 3D path planning Flight path generation Virtual endoscopy

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From early biological models to CNNs: Do they look where humans look? 25

From early biological models to CNNs: Do they look where hum...

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25th International Conference on Pattern Recognition, ICPR 2020

作者： Cadoni, Marinella Iole Lagorio, Andrea Grosso, Enrico Jia Huei, Tan Chee Seng, Chan Computer Vision Laboratory University of Sassari Sassari Italy Center of Image and Signal Processing Department of Artificial Intelligence Faculty of Computer Science and Information Technology University of Malaya Malaysia

ISBN: (纸本)9781728188089

Early hierarchical computational visual models as well as recent deep neural networks have been inspired by the functioning of the primate visual cortex system. Although much effort has been made to dissect neural networks to visualize the features they learn at the individual units, the scope of the visualizations has been limited to a categorization of the features in terms of their semantic level. Considering the ability humans have to select high semantic level regions of a scene, the question whether neural networks can match this ability, and if similarity with humans attention is correlated with neural networks performance naturally arise. To address this question we propose a pipeline to select and compare sets of feature points that maximally activate individual networks units to human fixations. We extract features from a variety of neural networks, from early hierarchical models such as HMAX up to recent deep convolutional neural netwoks such as Densnet, to compare them to human fixations. Experiments over the ETD database show that human fixations correlate with CNNs features from deep layers significantly better than with random sets of points, while they do not with features extracted from the first layers of CNNs, nor with the HMAX features, which seem to have low semantic level compared with the features that respond to the automatically learned filters of CNNs. It also turns out that there is a correlation between CNN's human similarity and classification performance. © 2020 IEEE

关键词： Deep neural networks

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Stochastic deformable model

Stochastic deformable model

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2005 16th British Machine vision Conference, BMVC 2005

作者： El-Baz, Ayman Farag, Aly Gimelfarb, Georgy Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292 United States Department of Computer Science Tamaki Campus University of Auckland Auckland New Zealand

ISBN: (纸本)1901725294

Deformable or active contour, and surface models are powerful image segmentation techniques. We introduce a novel fast and robust bi-directional parametric deformable model which is able to segment regions of intricate shape in multi-modal greyscale images. The power of the algorithm in terms of computation time and robustness is owing to the use of joint probabilities of the signals and region labels in individual points as external forces guiding the model evolution. These joint probabilities are derived from a Markov-Gibbs random field (MGRF) image model considering an image as a sample of two interrelated spatial stochastic processes. The low level process with conditionally independent and arbitrarily distributed signals relates to the observed image whereas its hidden map of regions is represented with the high level MGRF of interdependent region labels. Marginal probability distributions of signals in each region are recovered from a mixed empirical signal distribution over the whole image. In so doing, each marginal is approximated with a linear combination of Gaussians (LCG) having both positive and negative components. The LCG parameters are estimated using our previously proposed modification of the EM algorithm, and the high-level Gibbs potentials are computed analytically. Comparative experiments show that the proposed model outlines complicated boundaries of different modal objects much more accurately than other known counterparts.

关键词： Random processes

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Experiments on sensitivity of template matching for lung nodule detection in low dose CT scans

Experiments on sensitivity of template matching for lung nod...

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ISSPIT 2007 - 2007 IEEE International Symposium on Signal processing and Information Technology

作者： Elhabian, Shireen Y. El Munim, Hossam Abd Elshazly, Salwa Farag, Aly A. Aboelghar, Mohamed Computer Vision and Image Processing Laboratory Univ. of Louisville Louisville KY 40292 United States Urology and Nephrology Center Mansoura University Mansoura Egypt

ISBN: (纸本)9781424418350

Template matching is a common approach for detection lung nodules from CT scans. Templates may take different shapes, size and intensity distribution. The process of nodule detection is essentially two steps: isolation of candidate nodules, and elimination of false positive nodules. The processes of outlining the detected nodules and their classification (i.e., assigning pathology for each nodule) complete the CAD system for early detection of lung nodules. This paper is concerned with the template design and evaluating the effectiveness of the first step in the nodule detection process. The paper will neither address the problem of reducing false positives nor would it deal with nodule segmentation and classification. Only parametric templates are considered. Modeling the gray scale distribution for the templates based on the prior knowledge of typical nodules extracted by radiologists. The effectiveness of the template matching is investigated by cross validation with respect to the ground truth and is described by hit rate curves indicating the probability of detection as function of shape, size and orientation, if applicable, of the templates. We used synthetic and sample real CT scan images in our experiments. It is found that template matching is more sensitive to additive noise than image blurring when tests conducted on synthetic data. On the sample CT scans small size circular and hollow-circular templates provided comparable results to human experts. ©2007 IEEE.

关键词： Template matching

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What can an image of a smooth Lambertian surface tell about camera nonlinearity?

What can an image of a smooth Lambertian surface tell about ...

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2012 9th Conference on computer and Robot vision, CRV 2012

作者： El-Melegy, Moumen T. Farag, Aly A. Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292 United States Electrical Engineering Department Assiut University Assiut 71516 Egypt

ISBN: (纸本)9780769546834

This paper investigates the use of a single image of a smooth Lambertian surface to calibrate and remove some image nonlinearities due to the imaging device. To the best of our knowledge, this has not been addressed before in the literature. We show that this is possible, both theoretically and practically, taking advantage of some local shading measures that vary nonlinearly as a function of luminance and geometric nonlinearities (e.g., gamma correction and lens distortion). This can work as a basis for developing a simple method to estimate these nonlinearities from a single image. Several experiments are reported to validate the proposed method. © 2012 IEEE.

关键词： Cameras

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2D-PCA shape models: Application to 3D reconstruction of the human teeth from a single image

2D-PCA shape models: Application to 3D reconstruction of the...

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3rd International MICCAI Workshop on Medical computer vision, MCV 2013

作者： Abdelrehim, Aly S. Farag, Aly A. Shalaby, Ahmed M. El-Melegy, Moumen T. Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292 United States Electrical Engineering Department Assiut University Assiut 71516 Egypt

ISBN: (纸本)9783319055299

Accurate modeling of human teeth is mandatory for many reasons. Some of them are (1) Providing comfort to patients during mold process, and (2) Enhancing the accuracy level for oral orthodontist and dental care personnel. Shape from Shading (SFS) proved to be of great help in this problem, considering the fact that human tooth surface is textureless. In this paper, we address the problem of 3D tooth reconstruction, and improve the current algorithms. The approached developed in this paper reconstructs the teeth from single image shading with 2D-PCA shape priors that have more sophisticated reflectance model. Oren-Nayar-Wolff model was used for modeling the surface reflectance. This formulation uses shape priors as got from a set of training CT scans of real human teeth. Our experiments show promising quantitative results, which builds the infrastructure for having an optical based approach that accounts for inexpensive and radiationless human tooth reconstruction. © 2014 Springer International Publishing Switzerland.

关键词： computerized tomography

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Heat kernels for non-rigid shape retrieval: Sparse representation and efficient classification

Heat kernels for non-rigid shape retrieval: Sparse represent...

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2012 9th Conference on computer and Robot vision, CRV 2012

作者： Abdelrahman, Mostafa El-Melegy, Moumen Farag, Aly Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292 United States Electrical Engineering Department Assiut University Assiut 71516 Egypt

ISBN: (纸本)9780769546834

One of the major goals of computer vision and machine intelligence is the development of flexible and efficient methods for shape representation. This paper presents an approach for shape retrieval based on sparse representation of scale-invariant heat kernel. We use the Lap lace-Beltrami eigen functions to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales, combined with the normalized eigen values of the Lap lace-Beltrami operator. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the collaborative representation-based classification with regularized least square algorithm. We compare our approach to two well-known approaches on two different data sets: the nonrigid world data set and the SHREC 2011. The results have indeed confirmed the improved performance of the proposed approach, yet reducing the time and space complicity of the shape retrieval problem. © 2012 IEEE.

关键词： computer vision

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Experiments in multimodality image classification and data fusion

Experiments in multimodality image classification and data f...

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International Conference on Information Fusion

作者： A.A. Farag R. Mohamed H. Mahdi Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Computer Vision and Image Processing Laboratory University of Louisville Louisville KY

In this paper, we report the results of some experiments on image classification and data fusion of remote sensing images, as part of ongoing efforts at the CVIP to develop a general strategy for the analysis of multimodality imaging. Statistical and Fuzzy logic approaches have been employed in these experiments. In all, six different algorithms for image classification, and an image fusion algorithm have been implemented and evaluated on common data sets. These algorithms are: (1) Supervised Parametric Bayes Classifier; (2) Non-parametric Bayesian Classifier using the Parzen density estimate; (3) Maximum a posteriori classification using the k-nearest neighbors (k-NN) approach; (4) MAP Estimation using Markov random field modeling; (5) a Fuzzy logic approach; and (6) a novel discriminate function classifier. The AMP segmentation of the regions in the image has been implemented using the Iterated Conditional Mode (ICM) optimization method. This approach provided the best results, in terms of the minimum probability of error and best reliability. A novel decision fusion algorithm, based on the a priori class conditional probability, has been applied to the classifiers' output.

关键词： image classification Fuzzy logic Remote sensing image analysis Classification algorithms image fusion Bayesian methods Markov random fields image segmentation Optimization methods

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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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Multimodality image registration and fusion using neural network

Multimodality image registration and fusion using neural net...

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3rd International Conference on Information Fusion, FUSION 2000

作者： Mostafa, Mostafa G. Farag, Aly A. Essock, Edward Computer Vision and Image Processing Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY 40292 United States Department of Psychology University of Louisville Louisville KY 40292 United States

ISBN: (纸本)2725700000

Multi-modality image registration and fusion are essential steps in building 3D models from remote sensing data. In this paper, we present a neural network technique for the registration and fusion of multi-modality remote sensing data for the reconstruction of 3D models of terrain regions. A feedforward neural network is used to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data are presented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusion results. © 2000 Int. Soc. Inf. Fusion.

关键词： image fusion

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