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Cairo International Biomedical engineering Conference, CIBEC

作者： Amal Farag James Graham Hossam Abdelmunim Salwa Elshazly Mohamed Ei-Mogy Sabry Ei-Mogy Robert Falk Aly A. Farag Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY Computer & Systems Engineering Department Ain Shams University Cairo Mogy-Scan Center Mansoura Egypt Jewish Hospital and 3DR Louisville Kentucky

In this paper examination of the template modeling process using the Active Appearance Modeling (AAM) approach for automatic detection of lung nodules is investigated. A template matching approach is formulated to compute a similarity score between the AAM templates and the input lung CT slice, where the goal is to maximize the similarity measure at different image pixels to increase nodule detection. The template matching approach is implemented using nine similarity measures. Performance validation for the robustness of the generated models is tested on three clinical databases.

关键词： Lungs Databases Computed tomography Principal component analysis Computational modeling Solid modeling

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Moving shadow detection by nonlinear Tone-Mapping

Moving shadow detection by nonlinear Tone-Mapping

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International Conference on Systems, Signals and image processing, IWSSIP

作者： Elad Bullkich Idan Ilan Yair Moshe Yacov Hel-Or Hagit Hel-Or Signal and Image Processing Laboratory (SIPL) Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel Google Inc. Mountain View USA Department of Computer Science University of Haifa Haifa Israel

Shadow detection is a well-known problem in image and video processing. Detecting moving shadows is useful in numerous applications such as object detection and tracking. Most works in this area are not suitable for shadow detection using a low-cost outdoor surveillance camera. In this work we suggest a fast shadow detection approach for video surveillance, by comparing each video frame to a continuously updated background image. We differentiate shadow areas from foreground areas by assuming the shadow pixels are associated with background pixels through a nonlinear tone mapping. This assumption is general and applies to various systems and scene conditions. A distance measure between patch images that account for nonlinear tone mapping is calculated by adapting a recently suggested approach for pattern matching termed Matching by Tone Mapping (MTM). We show that the proposed technique is computationally efficient and outperforms state-of-the-art shadow detection techniques in typical surveillance scenarios.

关键词： Surveillance Measurement image color analysis Cameras Robustness Lighting

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Fully automated region growing segmentation of brain lesion in diffusion-weighted MRI

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2012年第2期39卷 155-164页

作者： Mohd Saad, N. Abu-Bakar, S.A.R. Muda, Sobri Mokji, M. Abdullah, A.R. Electronics and Computer Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia Faculty of Electrical Engineering Computer Vision Video and Image Processing Research Lab Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Radiology Department Medical Faculty Universiti Kebangsaan Malaysia 56000 Cheras Kuala Lumpur Malaysia Electrical Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia

This paper presents a fully automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI). The lesions are infarction, hemorrhage, tumor and abscess. Pre-processing stage is performed for intensity normalization, background removal and intensity enhancement. Then, split and merge algorithm is designed. Several statistical features are discussed and evaluated to select the best feature as homogeneity criteria. Lesions are segmented by merging the homogenous regions according to the selected criteria. This process produces blocky lesion region. Then, histogram thresholding is acquired to automate the seeds selection for region growing process. The region is iteratively grown by comparing all unallocated neighboring pixels to the seeds. The difference between pixel's intensity value and the region's mean is used as the similarity measure. The proposed segmentation technique has been validated by using misclassified area (MA), false positive rate (FPR), false negative rate (FNR), mean absolute percentage error (MAPE) and pixel absolute error ratio (rerr), and compared with previous methods. The result shows that automatic region growing method can successfully segment the lesions and is suitable for analysis and classification of DWI.

关键词： Magnetic resonance imaging

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Single camera-based full depth map estimation using color shifting property of a multiple color-filter aperture

Single camera-based full depth map estimation using color sh...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Seungwon Lee Junghyun Lee Monson H. Hayes Aggelos K. Katsaggelos Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

A multiple color-filter aperture (MCA) camera can provide depth information as well as color and intensity in the single-camera framework, where the MCA generates misalignment between color channels depending on the distance of a region-of-interest. In this paper, we present a single camera-based estimation of the full depth map using the color shifting property of the MCA. For estimating the color shifting vectors (CSVs) among red, green, and blue color channels, edges are extracted at each color channel. At the edge, we estimate CSVs using normalized cross correlation combined with color shifting mask map. A full depth map is then generated by depth interpolation using the matting Laplacian method from sparsely estimated CSVs at an edge location. Experimental results show that the proposed method can not only estimate the full depth map but also correct the misaligned color image to generate photorealistic color images using a single camera equipped with MCA.

关键词： image color analysis Cameras Estimation Channel estimation image edge detection Interpolation Apertures

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Hippocampal shape analysis in the Laplace Beltrami feature space for temporal lobe epilepsy diagnosis and lateralization

Hippocampal shape analysis in the Laplace Beltrami feature s...

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IEEE International Symposium on Biomedical Imaging

作者： Rosita Shishegar Ziba Gandomkar Hamid Soltaman-Zadeh Seyed Reza Moghadasi Control and Intelligent Processing Centre of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Department of Mathematics Sharif University of Technology Tehran Iran

Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami operator which is also used in this paper for hippocampal shape comparison. We proposed a feature vector which consists of size measures and shape descriptors based on Laplace Beltrami eigenvalues and eigenfunctions. The aforementioned feature space is utilised for automatic differentiating normal subjects from epileptic patients as well as distinguishing epileptic patients with left temporal lobe epilepsy (LTLE) from patients with right temporal lobe epilepsy (RTLE). Achieved results are diagnostic accuracy of 93% with 95% sensitivity and lateralization accuracy of 86%. The results are comparable to spherical harmonic based shape analysis results and notably improved in comparison to volumetric analysis results.

关键词： Shape Temporal lobe Epilepsy Abstracts Indexes Eigenvalues and eigenfunctions Feature extraction

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Voxel-based treatment prediction of glioblastoma multiform tumor using diffusion tensor imaging

Voxel-based treatment prediction of glioblastoma multiform t...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Hadi Sabahi Hamid Soltanian-Zadeh Lisa Scarpace Tom Mikkelsen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI USA Hermelin Brain Tumor Research Center Neurosurgery Department Henry Ford Health System Detroit MI USA

This paper proposes a method to predict the effect of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The prediction is critical for effective treatment planning. The proposed method is developed and evaluated using Diffusion Tensor Imaging (DTI) and post-contrast T1-weighted Magnetic Resonance images (pc-T1-MRI) of 14 patients with GBM tumors gathered before and after the treatment. First, the proposed method calculates diffusion anisotropy indices (DAI) of all voxels in the brain. These diffusion anisotropy indices are Fractional Anisotropy (FA), Mean Diffusivity (MD), Relative Anisotropy (RA), and Volume Ratio (VR). Then, it registers post-treatment pc-T1-MRI and pre-treatment DAI maps to pre-treatment pc-T1-MRI. Next, it uses a thresholding method to segment the tumor from pc-T1-MRI studies. Comparing Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI, the DAIs of the tumor are labeled based on their response to the treatment. The voxels of 7 patients are randomly selected to train 4 classifiers (ANN, SVM, KNN, and ANFIS) and then all voxels of the other 7 patients are used to test them. For each classifier, four performance measures (sensitivity, specificity, positive predictive value, and accuracy) are calculated. Experimental results show that the ANFIS is more accurate than the other classifiers in predicting the treatment response.

关键词： Support vector machines Noise Magnetic resonance imaging Sensitivity Tumors Biomedical imaging Artificial neural networks

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Automatic organs' detection in WCE

Automatic organs' detection in WCE

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International Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Omid Haji-Maghsoudi Alireza Talebpour Hamid Soltanian-Zadeh Navid Haji-maghsoodi Department of Radiation Medicine Shahid Beheshti University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Hospital Detroit MI USA Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Mechatronics Sharif University of Tehran Tehran Iran

Wireless capsule endoscopy (WCE) views the entire gastrointestinal (GI) tract. A main problem associated with this novel device is that too many frames must be reviewed by physicians. Thus it is essential to find an automatic and intelligent method to help physicians. One of the problems in WCE is its difficulty to distinguish among different organ's tissues. So, we introduce two novel algorithms which are able to classify main organs (among esophagus, stomach, small bowel and colon) in WCE's frames. In order to obtain our aim, we use statistic features (Haralick features) and non-statistic features (different diagrams and Gabor filter banks), colored features and non-colored features. Our experimental studies indicate good results that are shown in conclusion.

关键词： Level set Medical services Gabor filters Active contours Wireless communication Optical wavelength conversion Esophagus

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Elastic shape registration using an incremental free form deformation approach with the ICP algorithm

Elastic shape registration using an incremental free form de...

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Canadian Conference on computer and Robot vision

作者： Abdelmunim, Hossam Farag, Aly A. Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo 11517 Egypt Computer Vision and Image Processing Laboratory Electrical and Computer Engineering Department University of Louisville KY 40292 United States

ISBN: (纸本)9780769543628

Shape registration is one of the most challenging problems in computer vision and medical imaging. The process is affected by the way the shape is represented and the form of transformation used to move the source shape. Our paper handles the elastic shape registration by combining the incremental free form deformation (IFFD) with the point-based registration technique using the sum of least squares method. The iterative closest point (ICP) algorithm is used as a criteria to establish point correspondences in each level of the IFFD framework. The free form deformation (FFD) is well known in the literature and works by forming a lattice of control points that can move and hence deform the domain grid points smoothly and uniformly under some shape constrains. The control lattice resolution is increased step by step to achieve a satisfactory deformation of the source shape to exactly match the target boundaries. Our point-based registration is based on least squares that measure the Euclidean distance between source and target boundaries in addition to the shape constrains. The minimization gives a closed form solution of the lattice control points positions. Promising results will be demonstrated for closed and open shapes and structures. The approach can also work for structures that contain multiple parts without any problems. © 2011 IEEE.

关键词： Medical imaging

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The texture classification using the fusion of decisions from different texture classifiers

The texture classification using the fusion of decisions fro...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology

作者： Pharsook, S. Kasetkasem, T. Larmsrichan, P. Siddhichai, S. Chanwimaluang, T. Isshiki, T. Department of Electrical Engineering Kasetsart University Thailand Image Processing Laboratory National Electronics and Computer Technology Center Thailand Department of Communication and Integrated Systems Tokyo Institue of Technology Japan

ISBN: (纸本)9781457704246

The improved of texture classification accuracy by using the probability weighted combination method of three texture features extraction consist of thE0020 Gray-Level Co-occurrence Matrix (GLCM), Semivariogram Function and Gaussian Markov Random Fields (GMRFs). Five different textures images are used in the experiment. The classifier that use for classify the extracted features in this research is Support Vector Machines (SVMs). The experimental result shows that the average accuracy of the combination method with probability weight up to 95.71%, which is better than the simple combination method about 2%. © 2011 IEEE.

关键词： Support vector machines

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Model-based multiview stereo via level sets with statistical shape prior

Model-based multiview stereo via level sets with statistical...

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IEEE International Conference on image processing

作者： Moumen El-Melegy Nagi Al-Ashwal Aly A. Farag Electrical Engineering Department Assiut University Asyut Egypt Computer Vision and Image Processing Laboratory University of Louisville KY USA

In this paper, we propose a methodology to incorporate 3D shape prior information in multi-view stereo. This is important for applications that deal with specific category of objects. The methodology is based on a new formulation of a level-set based energy functional. Shape prior model is then embedded in the energy functional to allow the reconstruction of an object with shape variations consistent with the training model examples. Several experiments to evaluate the proposed methodology are presented.

关键词： Shape image reconstruction Level set Stereo vision Solid modeling Training Ellipsoids

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