Asem M. Ali
University of Louisville
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Publication
Featured researches published by Asem M. Ali.
medical image computing and computer assisted intervention | 2007
Asem M. Ali; Aly A. Farag; Ayman El-Baz
We propose a novel kidney segmentation approach based on the graph cuts technique. The proposed approach depends on both image appearance and shape information. Shape information is gathered from a set of training shapes. Then we estimate the shape variations using a new distance probabilistic model which approximates the marginal densities of the kidney and its background in the variability region using a Poisson distribution refined by positive and negative Gaussian components. To segment a kidney slice, we align it with the training slices so we can use the distance probabilistic model. Then its gray level is approximated with a LCG with sign-alternate components. The spatial interaction between the neighboring pixels is identified using a new analytical approach. Finally, we formulate a new energy function using both image appearance models and shape constraints. This function is globally minimized using s/t graph cuts to get the optimal segmentation. Experimental results show that the proposed technique gives promising results compared to others without shape constraints.
international symposium on visual computing | 2008
Asem M. Ali; Aly A. Farag
We propose a new technique for unsupervised segmentation of the lung region from low dose computed tomography (LDCT) images. We follow the most conventional approaches such that initial images and desired maps of regions are described by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. But our focus is on more accurate model identification for the MGRF model and the gray level distribution model. To better specify region borders between lung and chest, each empirical distribution of volume signals is precisely approximated by a linear combination of Gaussians (LCG) with positive and negative components. LCG models parameters are estimated by the modified EM algorithm. Initial segmentation (labeled volume) based on the LCG models is then iteratively refined by using the MGRF with analytically estimated potentials. In this framework the graph cuts is used as a global optimization algorithm to find the segmented data (labeled data) that minimize a certain energy function, which integrates the LCG model and the MGRF model. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of the LDCT data is designed. Experiments on both phantom and 3D LDCT data sets show that the proposed segmentation approach is more accurate than other known alternatives.
international symposium on biomedical imaging | 2011
A.A. Farag; Asem M. Ali; James H. Graham; Aly A. Farag; Salwa Elshazly; Robert Falk
This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured by the normalized cross-correlation (NCC). Geometric feature descriptors (e.g., SIFT, LBP and SURF) are applied to the output of the detection step, in order to extract features from the nodule candidates, for further enhancement of output and possible reduction of false positives. Results on the clinical ELCAP database showed that the descriptors provide 2% enhancements in the specificity of the detected nodule above the NCC results when used in a k-NN classifier. Thus quantitative measures of enhancements of the performance of CAD models based on LDCT are now possible and are entirely model-based. Most importantly, our approach is applicable for classification of nodules into categories and pathologies.
international symposium on visual computing | 2009
Melih S. Aslan; Asem M. Ali; Ham M. Rara; Ben Arnold; Aly A. Farag; Rachid Fahmi; Ping Xiang
Bone mineral density (BMD ) measurements and fracture analysis of the spine bones are restricted to the Vertebral bodies (VBs ). In this paper, we present a novel and fast 3D segmentation framework of VBs in clinical CT images using the graph cuts method. The Matched filter is employed to detect the VB region automatically. In the graph cuts method, a VB (object) and surrounding organs (background) are represented using a gray level distribution models which are approximated by a linear combination of Gaussians (LCG) to better specify region borders between two classes (object and background). Initial segmentation based on the LCG models is then iteratively refined by using MGRF with analytically estimated potentials. In this step, the graph cuts is used as a global optimization algorithm to find the segmented data that minimize a certain energy function, which integrates the LCG model and the MGRF model. Validity was analyzed using ground truths of data sets (expert segmentation) and the European Spine Phantom (ESP ) as a known reference. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.
european conference on computer vision | 2008
Asem M. Ali; Aly A. Farag; Georgy L. Gimel'farb
Widespread use of efficient and successful solutions of Computer Vision problems based on pairwise Markov Random Field (MRF) models raises a question: does any link exist between the pairwise and higher order MRFs such that the like solutions can be applied to the latter models? This work explores such a link for binary MRFs that allow us to represent Gibbs energy of signal interaction with a polynomial function. We show how a higher order polynomial can be efficiently transformed into a quadratic function. Then energy minimization tools for the pairwise MRF models can be easily applied to the higher order counterparts. Also, we propose a method to analytically estimate the potential parameter of the asymmetric Potts prior. The proposed framework demonstrates very promising experimental results of image segmentation and can be used to solve other Computer Vision problems.
Computer Vision and Image Understanding | 2013
Eslam A. Mostafa; Riad I. Hammoud; Asem M. Ali; Aly A. Farag
This paper proposes an accurate, rotation invariant, and fast approach for detection of facial features from thermal images. The proposed approach combines both appearance and geometric information to detect the facial features. A texture based detector is performed using Haar features and AdaBoost algorithm. Then the relation between these facial features is modeled using a complex Gaussian distribution, which is invariant to rotation. Experiments show that our proposed approach outperforms existing algorithms for facial features detection in thermal images. The proposed approachs performance is illustrated in a face recognition framework, which is based on extracting a local signature around facial features. Also, the paper presents a comparative study for different signature techniques with different facial image resolutions. The results of this comparative study suggest the minimum facial image resolution in thermal images, which can be used in face recognition. The study also gives a guideline for choosing a good signature, which leads to the best recognition rate.
international conference on image processing | 2010
Melih S. Aslan; Asem M. Ali; Dongqing Chen; Ben Arnold; Aly A. Farag; Ping Xiang
Osteoporosis is a bone disease characterized by a reduction in bone mass, resulting in an increased risk of fractures. To diagnose the osteoporosis accurately, bone mineral density (BMD) measurements and fracture analysis (FA) of the Vertebral bodies (VBs) are required. In this paper, we propose a robust and 3D shape based method to segment VBs in clinical computed tomography (CT) images in order to make BMD measurements and FA accurately. In this experiment, image appearance and shape information of VBs are used. In the training step, 3D shape information is obtained from a set of data sets. Then, we estimate the shape variations using a distance probabilistic model which approximates the marginal densities of the VB and background in the variability region. In the segmentation step, the Matched filter is used to detect the VB region automatically. We align the detected volume with 3D shape prior in order to be used in distance probabilistic model. Then, the graph cuts method which integrates the linear combination of Gaussians (LCG), Markov Gibbs Random Field (MGRF), and distance probabilistic model obtained from 3D shape prior is used.
IEEE Transactions on Information Forensics and Security | 2014
Asem M. Ali
Face recognition in the wild can be defined as recognizing individuals unabated by pose, illumination, expression, and uncertainties from the image acquisition. In this paper, we propose a framework recognizing human faces under such uncertainties by focusing on the pose problem while considering the other factors together. The proposed work introduces an automatic front-end stereo-based system, which starts with image acquisition and ends by face recognition. Once an individual is detected by one of the stereo cameras, its facial features are identified using a facial features extraction model. These features are used to steer the second camera to see the same subject. Then, a stereo pair is captured and 3D face is reconstructed. The proposed stereo matching approach carefully handles illumination variance, occlusion, and disparity discontinuity. The reconstructed 3D shape is used to synthesize virtual 2D views in novel poses. All these steps are done off-line in an Enrollment stage. To recognize a face from a 2D image, which is captured under unknown environmental conditions, another fast on-line stage starts by facial features detection. Then, a facial signature is extracted from patches around these facial features. Finally, this probe image is matched against the closest synthesized images. Experiments are conducted on different public databases from where we investigate the effect of each component of the proposed framework on the recognition performance. The results confirm that without training and with automatic features extraction, our proposed face recognition at a distance approach outperforms most of the state-of-the-art approaches.
international conference on image processing | 2010
Melih S. Aslan; Asem M. Ali; Ham M. Rara; Aly A. Farag
In this paper, we propose a new 3D framework to identify and segment VBs and TBs in clinical computed tomography (CT) images without any user intervention. The Matched filter is employed to detect the VB region automatically on axial axis. To identify the VB on coronal and sagittal axis, we use a new developed approach based on 4 points automatically placed on cortical shell. To segment the identified VB, the graph cuts method which integrates a linear combination of Gaussians (LCG) and Markov Gibbs Random Field (MGRF) are used. Then, the cortical and trabecular bones are segmented using local volume growing methods. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.
computer vision and pattern recognition | 2009
Ham M. Rara; Shireen Y. Elhabian; Asem M. Ali; Mike Miller; Thomas L. Starr; Aly A. Farag
We describe a framework for face recognition at a distance based on sparse-stereo reconstruction. We develop a 3D acquisition system that consists of two CCD stereo cameras mounted on pan-tilt units with adjustable baseline. We first detect the facial region and extract its landmark points, which are used to initialize an AAM mesh fitting algorithm. The fitted mesh vertices provide point correspondences between the left and right images of a stereo pair; stereo-based reconstruction is then used to infer the 3D information of the mesh vertices. We perform experiments regarding the use of different features extracted from these vertices for face recognition. The cumulative rank curves (CMC), which are generated using the proposed framework, confirms the feasibility of the proposed work for long distance recognition of human faces with respect to the state-of-the-art.