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IEEE Convention of the electrical and ELectronic Engineers in Israel

作者： Rami Cohen Yizhar Lavner Signal and Image Processing Laboratory (SIPL) Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel Department of Computer Science Tel-Hai Academic College Upper Galilee Israel

In this paper we propose an algorithm for automatic detection of an infant cry. A particular application of this algorithm is the identification of a physical danger to babies, such as situations in which parents leave their children in vehicles. The proposed algorithm is based on two main stages. The first stage involves feature extraction, in which pitch related parameters, MFC (mel-frequency cepstrum) coefficients and short-time energy parameters are extracted from the signal. In the second stage, the signal is classified using the k-NN algorithm and is later verified as a cry signal, based on the pitch and harmonics information. In order to evaluate the performance of the algorithm in real world scenarios, we checked the robustness of the algorithm in the presence of several types of noise, and especially noises such as car horns and car engines that are likely to be present in vehicles. In addition, we addressed real time and low complexity demands during the development of the algorithm. In particular, we used a voice activity detector, which disabled the operation of the algorithm when voice activity was not present. A database of baby cry signals was used for performance evaluation. The results showed good performance of the proposed algorithm, even at low SNR.

关键词： Pediatrics Harmonic analysis Noise Feature extraction Mel frequency cepstral coefficient Detection algorithms Cepstrum

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3D computational modeling of epiretinal prosthesis stimulation of the ganglion cell

3D computational modeling of epiretinal prosthesis stimulati...

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

作者： Shabnam Novin Hamid Abrishami Moghaddam Amir Masoud Sodagar Amin Mahnam Research Laboratory for Integrated Circuits and Systems (I CAS) Department of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran Research Laboratory for Machine Vision and Medical Image Processing (MVMIP) Department of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran Ecole Polytechnique de Montreal Montreal QUE Canada Department of Biomedical Engineering School of Engineering University of Isfahan Isfahan Iran

Here we present a modeling for electrical stimulation of retinal ganglion cell. This modeling can be used in epiretinal prosthesis to come up with proper electrical stimulation parameters. The model that we present is a novel one based on the anatomy of the cell. The excitation process of the electrically stimulated retinal cells is simulated in a two step procedure. In the first step the ganglion target cell is represented considering all of its compartments and in a second step the extracellular potential along the neural structure is calculated and finally the membrane voltage response to the applied stimulations is analyzed. A monopolar point electrode was positioned above the cell in an infinite and homogenous environment and the monophasic and biphasic pulses were used as stimulating current. The results show that the responses are in accordance with the values and diagrams of experimental results more than the previous models.

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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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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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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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Development of a MATLAB-Based Toolbox for Brain computer Interface Applications in Virtual Reality

Development of a MATLAB-Based Toolbox for Brain Computer Int...

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Iranian Conference on electrical engineering

作者： Fardin Afdideh Mohammad Bagher Shamsollahi Seyed Navid Resalat Biomedical Signal and Image Processing Laboratory (BiSPL) School of Electrical Engineering Sharif University of Technology Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran

ISBN: (纸本)9781467311496

Brain computer interface (BCI) is a widely used system to assist the disabled and paralyzed people by creating a new communication channel. Among the various methods used in BCI area, motor imagery (MI) is the most popular and the most common one due to its the most natural way of communication for the subject. Some software applications are used to implement BCI systems, and some toolboxes exist for EEG signal processing. In recent years virtual reality (VR) technology has entered into the BCI research area to simulate the real world situations and enhance the subject performance. In this work, a completely MATLAB-based Mi-based BCI system is proposed and implemented in order to navigate into a virtual environment. In addition, a variety of features types were employed to select the best ones in the proposed system with the use of linear discriminant analysis (LDA) classifier through some interactive graphical user interfaces (GUIs). The results show the feasibility of the proposed BCI system in the subject training with or without feedback and even navigation into a virtual home.

关键词： Brain computer Interface (BCI) MATLAB Motor imagery (MI) Virtual Reality (VR) Real-time EEG signal processing

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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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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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Distributed Estimation Over an Adaptive Diffusion Network Based on the Family of Affine Projection Algorithms

Distributed Estimation Over an Adaptive Diffusion Network Ba...

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International Symposium on Telecommunications

作者： Mohammad Shams Esfand Abadi Zahra Saffari Faculty of Electrical and Computer Engineering Shahid Rajaee Teacher Training University Adaptive Systems and Image Processing Research Laboratory Shahid Rajaee Teacher Training University

ISBN: (纸本)9781467320726

In this paper, we present the distributed selective partial update affine projection algorithm (dSPU-APA), the distributed dynamic selection affine projection algorithm (dDS-APA), and dSPU-DS-APA to solve distributed estimation problem over adaptive diffusion networks. In dSPU-APA, the filter coefficients are partially updated during the adaptation. For dDS-APA, the input regressors are dynamically selected at each iteration. Diffusion cooperation schemes have been shown to provide good performance in these algorithms. The good performance of these algorithms is demonstrated by presenting several simulation results.

关键词： Distributed selective partial affine projection algorithm Dynamic selection Distributed estimation Adaptive diffusion networks Diffusion cooperation

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