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MMRO: A feature selection criterion for MR images based on alpha stable filter responses
, Article 2011 7th Iranian Conference on Machine Vision and Image Processing, MVIP 2011 - Proceedings ; 2011 ; 9781457715358 (ISBN) ; Fatemizadeh, E ; Sharif University of Technology
Abstract
In feature-based image registration, feature selection and reduction methods play an important role in decreasing computational burden of these operations. In this paper, a new approach is introduced to reduce the dimension of extracted feature vectors of MR images. This approach is based on the selection of the maximum and minimum responses of the alpha stable filter for the MR images over the extracted features with different orientation in frequency domain. This algorithm selects the rotation invariant features which are suitable for image registration purposes. It has been shown that these features could efficiently describe the image elements. The discriminating ability of the features...
Malignancy determination of tumors using perfusion MRI
, Article 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009, Las Vegas, NV, 13 July 2009 through 16 July 2009 ; Volume 2 , 2009 , Pages 906-909 ; 9781601321190 (ISBN) ; Soltanian Zadeh, H ; Akhlaghpour, S ; Fatemi Zadeh, E ; United States Military Academy, Network Science Center; HST Harvard Univ. MIT, Biomed. Cybern. Lab.; Argonne's Leadersh. Comput. Facil. Argonne Natl. Lab.; Univ. Illinois Urbana-Champaign, Funct. Genomics Lab.; University of Minnesota, Minnesota Supercomputing Institute ; Sharif University of Technology
2009
Abstract
Our purpose was to determine whether perfusion MR imaging can be used for malignancy determination of tumors. Relative cerebral blood flow (rCBF) is a commonly used perfusion magnetic resonance imaging (MRI) technique for the evaluation of malignancy. The goal of our study was to determine the usefulness of this parameter in malignancy determination of tumors using Independent Component Analysis (ICA)
Tracking the 3D configuration of human joint using an MR image registration technique
, Article ASME 2010 5th Frontiers in Biomedical Devices Conference and Exhibition, BIOMED 2010, 20 September 2010 through 21 September 2010 ; 2010 , Pages 93-94 ; 9780791849453 (ISBN) ; Farahmand, F ; Jafari, A ; Sharif University of Technology
Abstract
Surface registration is a necessary step and widely used in medical image-aided surgery. It's relevance to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities. In this study, the kinematic relations between two point clouds with different coordinate definitions have been generated. Using Influence Method of surface modeling for extracting point clouds functions, the transformation matrix would be resulted. The proposed method was applied for an experimental femur data points(651 points) using the MRI images. These data points were transformed in a 30 degrees flexion of knee. This...
Adaptive sparse representation for MRI noise removal
, Article Biomedical Engineering - Applications, Basis and Communications ; Volume 24, Issue 5 , October , 2012 , Pages 383-394 ; 10162372 (ISSN) ; Fatemizadeh, E ; Behnam, H ; Sharif University of Technology
World Scientific
2012
Abstract
Sparse representation is a powerful tool for image processing, including noise removal. It is an effective method for Gaussian noise removal by taking advantage of a fixed and learned dictionary. In this study, the variable distribution of Rician noise is reduced in magnetic resonance (MR) images by sparse representation based on reconstruction error sets. Standard deviation of Gaussian noise is used to find these errors locally. The proposed method represents two formulas for local error calculation using standard deviation of noise. The acquired results from the real and simulated images are comparable, and in some cases, better than the best Rician noise removal method due to the...
Obesity and spinal loads; a combined MR imaging and subject-specific modeling investigation
, Article Journal of Biomechanics ; 2017 ; 00219290 (ISSN) ; Kazemi, H ; Eskandari, A. H ; Arjmand, N ; Sharif University of Technology
Abstract
Epidemiological studies have identified obesity asa possible risk factor for low back disorders. Biomechanical models can help test such hypothesis and shed light on the mechanism involved. A novel subject-specific musculoskeletal-modelling approach is introduced to estimate spinal loads during static activities in five healthy obese (BMI>30kg/m2) and five normal-weight (20
Obesity and spinal loads; a combined MR imaging and subject-specific modeling investigation
, Article Journal of Biomechanics ; Volume 70 , March , 2018 , Pages 102-112 ; 00219290 (ISSN) ; Kazemi, H ; Eskandari, A. H ; Arjmand, N ; Sharif University of Technology
Elsevier Ltd
2018
Abstract
Epidemiological studies have identified obesity as a possible risk factor for low back disorders. Biomechanical models can help test such hypothesis and shed light on the mechanism involved. A novel subject-specific musculoskeletal-modelling approach is introduced to estimate spinal loads during static activities in five healthy obese (BMI > 30 kg/m2) and five normal-weight (20 < BMI < 25 kg/m2) individuals. Subjects underwent T1 through S1 MR imaging thereby measuring cross-sectional-area (CSA) and moment arms of trunk muscles together with mass and center of mass (CoM) of T1-L5 segments. MR-based subject-specific models estimated spinal loads using a kinematics/optimization-driven...
Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264
, Article Proceedings of SPIE - The International Society for Optical Engineering, 9 December 2011 through 10 December 2011, Singapore ; Volume 8349 , December , 2012 ; 0277786X (ISSN) ; 9780819490254 (ISBN) ; Kasaei, S ; Sharif University of Technology
Abstract
Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more...
Gradient vector flow snake segmentation of breast lesions in dynamic contrast-enhanced MR images
, Article 2010 17th Iranian Conference of Biomedical Engineering, ICBME 2010 - Proceedings, 3 November 2010 through 4 November 2010, Isfahan ; 2010 ; 9781424474844 (ISBN) ; Fatemizadeh, E ; Gity, M ; Sharif University of Technology
Abstract
The development of computer-aided diagnosis (CAD) for breast magnetic resonance (MR) images has encountered some big challenges. One of these challenges is related to breast lesion segmentation. Accurate segmentation of breast lesions has a vital role in other consequent applications such as feature extraction. Since malignant breast lesions typically appear with irregular borders and shapes in MR images whereas benign masses appear with more regular shapes, and smooth and lobulated borders, it seems that the accurate segmentation of breast lesion borders in MR images are important. To achieve this purpose, we have used the Gradient Vector Flow (GVF) snake segmentation method. This study...
Development of Alzheimer's disease recognition using semiautomatic analysis of statistical parameters based on frequency characteristics of medical images
, Article 2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007, Dubai, 14 November 2007 through 27 November 2007 ; 2007 , Pages 868-871 ; 9781424412365 (ISBN) ; Moradzadeh, H ; Vaziri, R ; Razavian, S. M. J ; Dehestani Ardekani, R ; Rahmandoust, M ; Taalimi, A ; Fatemizadeh, E ; Sharif University of Technology
2007
Abstract
The paper presents an effective algorithm to analyze MR-images in order to recognize Alzheimer's Disease (AD) which appeared in patient's brain. The features of interest are categorized in Features of the Spatial Domain (FSD's) and Features of the Frequency Domain (FFD's) which are based on the first four statistic moments of the wavelet transform. Extracted features have been classified by a multi-layer perceptron Artificial Neural Network (ANN). Before ANN, the number of features is reduced from 44 to 12 to optimize and eliminate any correlation between them. The contribution of this paper is to demonstrate that by using the wavelet transform number of features needed for AD diagnosis has...
A modified low rank learning based on iterative nuclear weighting in ripplet transform for denoising MR images
, Article 29th Iranian Conference on Electrical Engineering, ICEE 2021, 18 May 2021 through 20 May 2021 ; 2021 , Pages 912-916 ; 9781665433655 (ISBN) ; Nejati Jahromi, M ; Nouri, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
In recent studies, several methods have been suggested to decrease noise of magnetic resonance image (MRI) in order to raise the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM). In this paper, we propose a novel method based on a minimization problem in Ripplet domain that uses singular value decomposition (SVD) in low rank learning to eliminate the noise of MRI images. We reschedule the weighted nuclear norm minimization (WNNM) problem in any edges of Ripplet domain transform and using an adaptive weighting structure to denoise the patches of Ripplet component matrix. The parameters of the proposed method are divided into two groups, some of them are calculated...
Using type-2 fuzzy function for diagnosing brain tumors based on image processing approach
, Article 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, 18 2010 through 23 July 2010 ; July , 2010 ; 9781424469208 (ISBN) ; Zarinbal, M ; Zarinbal, A ; Turksen, I. B ; Izadi, M ; Sharif University of Technology
2010
Abstract
Fuzzy functions are used to identify the structure of system models and reasoning with them. Fuzzy functions can be determined by any function identification method such as Least Square Estimates (LSE), Maximum Likelihood Estimates (MLE) or Support Vector Machine Estimates (SVM). However, estimating fuzzy functions using LSE method is structurally a new and unique approach for determining fuzzy functions. By using this approach, there is no need to know or to develop an in-depth understanding of essential concepts for developing and using the membership functions and selecting the t-norms, co-norms and implication operators. Furthermore, there is no need to apply fuzzification and...
A detailed and validated three dimensional dynamic model of the patellofemoral joint
, Article Journal of Biomechanical Engineering ; Volume 134, Issue 4 , 2012 ; 01480731 (ISSN) ; Farahmand, F ; Jafari, A ; Foumani, M. S ; Sharif University of Technology
2012
Abstract
A detailed 3D anatomical model of the patellofemoral joint was developed to study the tracking, force, contact and stability characteristics of the joint. The quadriceps was considered to include six components represented by 15 force vectors. The patellar tendon was modeled using four bundles of viscoelastic tensile elements. Each of the lateral and medial retinaculum was modeled by a three-bundle nonlinear spring. The femur and patella were considered as rigid bodies with their articular cartilage layers represented by an isotropic viscoelastic material. The geometrical and tracking data needed for model simulation, as well as validation of its results, were obtained from an in vivo...
A multiscale phase field method for joint segmentation-rigid registration application to motion estimation of human knee joint
, Article Biomedical Engineering - Applications, Basis and Communications ; Volume 23, Issue 6 , 2011 , Pages 445-456 ; 10162372 (ISSN) ; Esfandiarpour, F ; Shakourirad, A ; Farahmand, F ; Sharif University of Technology
2011
Abstract
Image based registration of rigid objects has been frequently addressed in the literature to obtain an object's motion parameters. In this paper, a new approach of joint segmentation-rigid registration, within the variational framework of the phase field approximation of the Mumford-Shah's functional, is proposed. The defined functional consists of two Mumford-Shah equations, extracting the discontinuity set of the reference and target images due to a rigid spatial transformation. Multiscale minimization of the proposed functional after finite element discretization provided a sub-pixel, robust algorithm for edge extraction as well as edge based rigid registration. The implementation...