Dr. Mahesh B. Nagarajan Profile

Dr. Mahesh B. Nagarajan

SPIE Involvement:

Author

Publications (29)

Proceedings Article | 3 March 2017 Presentation + Paper

Characterizing cartilage microarchitecture on phase-contrast x-ray computed tomography using deep learning with convolutional neural networks

Botao Deng, Anas Abidin, Adora D'Souza, Mahesh Nagarajan, Paola Coan, Axel Wismüller

Proceedings Volume 10134, 1013412 (2017) https://doi.org/10.1117/12.2254645

KEYWORDS: Neurons, Convolutional neural networks, Cartilage, Neural networks, X-ray computed tomography, Phase contrast, Tissues, Machine learning, Visualization, Medical imaging, Image classification, Feature extraction

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Proceedings Article | 29 March 2016 Paper

Mutual connectivity analysis (MCA) using generalized radial basis function neural networks for nonlinear functional connectivity network recovery in resting-state functional MRI

Adora D'Souza, Anas Zainul Abidin, Mahesh Nagarajan, Axel Wismüller

Proceedings Volume 9788, 97880K (2016) https://doi.org/10.1117/12.2216900

KEYWORDS: Functional magnetic resonance imaging, Neural networks, Magnetic resonance imaging, Detection and tracking algorithms, Brain, Complex systems, Brain imaging, Brain mapping, Image processing, Image segmentation, Data modeling, Biological research, Radiology, Independent component analysis, Statistical analysis

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Proceedings Article | 29 March 2016 Paper

Detecting altered connectivity patterns in HIV associated neurocognitive impairment using mutual connectivity analysis

Anas Zainul Abidin, Adora D'Souza, Mahesh Nagarajan, Axel Wismüller

Proceedings Volume 9788, 97880N (2016) https://doi.org/10.1117/12.2217315

KEYWORDS: Functional magnetic resonance imaging, Brain, Statistical analysis, Time series analysis, Neuroimaging, Neurological disorders, Magnetic resonance imaging, Error analysis, Functional imaging, Data modeling, Radiology, Control systems, Biological research, Biomedical optics

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Proceedings Article | 29 March 2016 Paper

Investigating changes in brain network properties in HIV-associated neurocognitive disease (HAND) using mutual connectivity analysis (MCA)

Anas Zainul Abidin, Adora D'Souza, Mahesh Nagarajan, Axel Wismüller

Proceedings Volume 9788, 97881W (2016) https://doi.org/10.1117/12.2217317

KEYWORDS: Brain, Functional magnetic resonance imaging, Magnetic resonance imaging, Time series analysis, Neural networks, Brain imaging, Brain mapping, Radiology, Model-based design, Neurons, Biomedical optics, Control systems

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Proceedings Article | 24 March 2016 Paper

Assessing vertebral fracture risk on volumetric quantitative computed tomography by geometric characterization of trabecular bone structure

Walter Checefsky, Anas Abidin, Mahesh Nagarajan, Jan Bauer, Thomas Baum, Axel Wismüller

Proceedings Volume 9785, 978508 (2016) https://doi.org/10.1117/12.2216898

KEYWORDS: Bone, X-ray computed tomography, Computed tomography, Minerals, Data acquisition, Calibration, Image processing, Machine learning, Image segmentation, Radiology

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Proceedings Article | 19 March 2015 Paper

Volumetric characterization of human patellar cartilage matrix on phase contrast x-ray computed tomography

Anas Abidin, Mahesh Nagarajan, Walter Checefsky, Paola Coan, Paul Diemoz, Susan Hobbs, Markus Huber, Axel Wismüller

Proceedings Volume 9417, 94171F (2015) https://doi.org/10.1117/12.2082084

KEYWORDS: Cartilage, Feature extraction, Phase contrast, X-ray computed tomography, Visualization, Tissues, Binary data, Radiology, Matrices, Statistical analysis

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Proceedings Article | 17 March 2015 Paper

Nonlinear functional connectivity network recovery in the human brain with mutual connectivity analysis (MCA): convergent cross-mapping and non-metric clustering

Axel Wismüller, Anas Abidin, Adora D'Souza, Xixi Wang, Susan Hobbs, Lutz Leistritz, Mahesh Nagarajan

Proceedings Volume 9417, 94170M (2015) https://doi.org/10.1117/12.2082124

KEYWORDS: Functional magnetic resonance imaging, Brain, Shape memory alloys, Magnetic resonance imaging, Radiology, Data acquisition, Visualization, Neuroimaging, Biological research, Analytical research

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Proceedings Article | 17 March 2015 Paper

Functional connectivity analysis in resting state fMRI with echo-state networks and non-metric clustering for network structure recovery

Axel Wismüller, Adora DSouza, Anas Abidin, Xixi Wang, Susan Hobbs, Mahesh Nagarajan

Proceedings Volume 9417, 94171M (2015) https://doi.org/10.1117/12.2082106

KEYWORDS: Functional magnetic resonance imaging, Neurons, Brain, Magnetic resonance imaging, Visualization, Radiology, Neural networks, Linear filtering, Analytical research, Data acquisition

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Proceedings Article | 17 March 2015 Paper

Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography

Mahesh Nagarajan, Walter Checefsky, Anas Abidin, Halley Tsai, Xixi Wang, Susan Hobbs, Jan Bauer, Thomas Baum, Axel Wismüller

Proceedings Volume 9417, 94171E (2015) https://doi.org/10.1117/12.2082059

KEYWORDS: Bone, Computed tomography, Calibration, Radiology, Minerals, Matrices, Neural networks, Failure analysis, Image segmentation, Scanners

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Proceedings Article | 17 March 2015 Paper

Investigating the use of mutual information and non-metric clustering for functional connectivity analysis on resting-state functional MRI

Xixi Wang, Mahesh Nagarajan, Anas Abidin, Adora DSouza, Susan Hobbs, Axel Wismüller

Proceedings Volume 9417, 94171N (2015) https://doi.org/10.1117/12.2082565

KEYWORDS: Functional magnetic resonance imaging, Brain, Magnetic resonance imaging, Brain mapping, Associative arrays, Radiology, Signal attenuation, Dimension reduction, Biological research, Neuroimaging

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Proceedings Article | 9 April 2014 Paper

Investigating the use of texture features for analysis of breast lesions on contrast-enhanced cone beam CT

Xixi Wang, Mahesh Nagarajan, David Conover, Ruola Ning, Avice O'Connell, Axel Wismueller

Proceedings Volume 9038, 903822 (2014) https://doi.org/10.1117/12.2042397

KEYWORDS: Breast, Tissues, Mammography, 3D image processing, Feature extraction, Computed tomography, Machine learning, Image classification, Image segmentation, Biological research

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Proceedings Article | 9 April 2014 Paper

Using anisotropic 3D Minkowski functionals for trabecular bone characterization and biomechanical strength prediction in proximal femur specimens

Mahesh Nagarajan, Titas De, Eva-Maria Lochmüller, Felix Eckstein, Axel Wismüller

Proceedings Volume 9038, 903820 (2014) https://doi.org/10.1117/12.2044352

KEYWORDS: Bone, Anisotropy, Head, Biomedical optics, Binary data, Medical imaging, Radiology, 3D image processing, Biological research, Computed tomography

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Proceedings Article | 13 March 2014 Paper

Pair-wise clustering of large scale Granger causality index matrices for revealing communities

Axel Wismüller, Mahesh Nagarajan, Herbert Witte, Britta Pester, Lutz Leistritz

Proceedings Volume 9038, 90381R (2014) https://doi.org/10.1117/12.2044340

KEYWORDS: Matrices, Autoregressive models, Principal component analysis, Magnetic resonance imaging, Data processing, Biological research, Biomedical optics, Distance measurement, Image processing, Associative arrays

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Proceedings Article | 13 March 2014 Paper

Predicting the biomechanical strength of proximal femur specimens with Minkowski functionals and support vector regression

Chien-Chun Yang, Mahesh Nagarajan, Markus Huber, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Felix Eckstein, Eva-Maria Lochmüller, Thomas Link, Axel Wismüller

Proceedings Volume 9038, 903810 (2014) https://doi.org/10.1117/12.2041782

KEYWORDS: Bone, Head, Computed tomography, Machine learning, Feature extraction, Biomedical optics, Radiology, Biological research, Failure analysis, 3D image processing

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Proceedings Article | 13 March 2014 Paper

Method and device for intraoperative imaging of lumpectomy specimens to provide feedback to breast surgeon for prompt re-excision during the same procedure

Andrzej Krol, Susan Hemingway, Kara Kort, Gustavo de la Rosa, Ravi Adhikary, Deepa Masrani, David Feiglin, Avice O’Connell, Mahesh Nagarajan, Chien-Chun Yang, Axel Wismüller

Proceedings Volume 9038, 90381G (2014) https://doi.org/10.1117/12.2043919

KEYWORDS: Breast, Tissues, Surgery, Digital breast tomosynthesis, Optical spheres, Imaging devices, Foam, Stereoscopy, Mammography, Tumors

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Breast conserving therapy (BCT) of breast cancer is now widely accepted due to improved cosmetic outcome and improved patients’ quality of life. One of the critical issues in performing breast-conserving surgery is trying to achieve microscopically clear surgical margins while maintaining excellent cosmesis. Unfortunately, unacceptably close or positive surgical margins occur in at least 20-25% of all patients undergoing BCT requiring repeat surgical excision days or weeks later, as permanent histopathology routinely takes days to complete. Our aim is to develop a better method for intraoperative imaging of non-palpable breast malignancies excised by wire or needle localization. Providing non-deformed three dimensional imaging of the excised breast tissue should allow more accurate assessment of tumor margins and consequently allow further excision at the time of initial surgery thus limiting the enormous financial and emotional burden of additional surgery. We have designed and constructed a device that allows preservation of the excised breast tissue in its natural anatomic position relative to the breast as it is imaged to assess adequate excision. We performed initial tests with needle-guided lumpectomy specimens using micro-CT and digital breast tomosynthesis (DBT). Our device consists of a plastic sphere inside a cylindrical holder. The surgeon inserts a freshly excised piece of breast tissue into the sphere and matches its anatomic orientation with the fiducial markers on the sphere. A custom-shaped foam is placed inside the sphere to prevent specimen deformation due to gravity. DBT followed by micro-CT images of the specimen were obtained. We confirmed that our device preserved spatial orientation of the excised breast tissue and that the location error was lower than 10mm and 10 degrees. The initial obtained results indicate that breast lesions containing microcalcifications allow a good 3D imaging of margins providing immediate intraoperative feedback for further excision as needed at the initial operation.

Proceedings Article | 13 March 2014 Paper

Phase contrast imaging X-ray computed tomography: quantitative characterization of human patellar cartilage matrix with topological and geometrical features

Mahesh Nagarajan, Paola Coan, Markus Huber, Paul Diemoz, Axel Wismüller

Proceedings Volume 9038, 903811 (2014) https://doi.org/10.1117/12.2042395

KEYWORDS: Cartilage, Feature extraction, Machine learning, Phase contrast, X-ray computed tomography, Visualization, Tissues, Binary data, Biomedical optics, Radiology

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SPIE Journal Paper | 4 February 2014

Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression

Chien-Chun Yang, Mahesh Nagarajan, Markus Huber, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Felix Eckstein, Eva-Maria Lochmüller, Sharmila Majumdar, Thomas Link, Axel Wismüller

JEI, Vol. 23, Issue 01, 013013, (February 2014) https://doi.org/10.1117/12.10.1117/1.JEI.23.1.013013

KEYWORDS: Bone, Dual energy x-ray absorptiometry, Head, Feature extraction, Neck, Machine learning, Computed tomography, Tissues, Minerals, Performance modeling

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Proceedings Article | 29 March 2013 Paper

Characterizing healthy and osteoarthritic knee cartilage on phase contrast CT with geometric texture features

Mahesh Nagarajan, Paola Coan, Markus Huber, Paul Diemoz, Christian Glaser, Axel Wismüller

Proceedings Volume 8672, 86721J (2013) https://doi.org/10.1117/12.2006255

KEYWORDS: Cartilage, Statistical analysis, Feature extraction, Computed tomography, Machine learning, Phase contrast, Visualization, Fuzzy logic, Tissues, Biomedical optics

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Proceedings Article | 29 March 2013 Paper

Predicting the biomechanical strength of proximal femur specimens through high dimensional geometric features and support vector regression

Chien-Chun Yang, Mahesh Nagarajan, Markus Huber, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Felix Eckstein, Eva Lochmüller, Sharmila Majumdar, Thomas Link, Axel Wismüller

Proceedings Volume 8672, 86721K (2013) https://doi.org/10.1117/12.2006265

KEYWORDS: Bone, Biological research, Head, Biomedical optics, Failure analysis, Machine learning, Feature extraction, Radiology, Computed tomography, Minerals

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Proceedings Article | 29 March 2013 Paper

Introducing anisotropic Minkowski functionals and quantitative anisotropy measures for local structure analysis in biomedical imaging

Axel Wismüller, Titas De, Eva Lochmüller, Felix Eckstein, Mahesh Nagarajan

Proceedings Volume 8672, 86720I (2013) https://doi.org/10.1117/12.2007192

KEYWORDS: Anisotropy, Bone, Head, Neck, Biomedical optics, Medical imaging, Biological research, Image processing, Visualization, Feature extraction

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Proceedings Article | 16 April 2012 Paper

Characterization of healthy and osteoarthritic chondrocyte cell patterns on phase contrast CT images of the knee cartilage matrix

Mahesh Nagarajan, Paola Coan, Markus Huber, Chien-Chun Yang, Christian Glaser, Maximilian Reiser, Axel Wismüller

Proceedings Volume 8317, 831720 (2012) https://doi.org/10.1117/12.910919

KEYWORDS: Cartilage, Feature extraction, Computed tomography, Machine learning, Visualization, Fuzzy logic, Statistical analysis, Phase contrast, Tissues, Image classification

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Proceedings Article | 23 February 2012 Paper

Predicting the biomechanical strength of proximal femur specimens with bone mineral density features and support vector regression

Markus Huber, Chien-Chun Yang, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Mahesh Nagarajan, Felix Eckstein, Eva Lochmüller, Sharmila Majumdar, Thomas Link, Axel Wismüller

Proceedings Volume 8315, 83151W (2012) https://doi.org/10.1117/12.911402

KEYWORDS: Bone, Minerals, Head, Feature extraction, Computing systems, Computed tomography, Computer aided diagnosis and therapy, Failure analysis, Statistical analysis, Image segmentation

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To improve the clinical assessment of osteoporotic hip fracture risk, recent computer-aided diagnosis systems explore new approaches to estimate the local trabecular bone quality beyond bone density alone to predict femoral bone strength. In this context, statistical bone mineral density (BMD) features extracted from multi-detector computed tomography (MDCT) images of proximal femur specimens and different function approximations methods were compared in their ability to predict the biomechanical strength. MDCT scans were acquired in 146 proximal femur specimens harvested from human cadavers. The femurs' failure load (FL) was determined through biomechanical testing. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone was represented by statistical moments of the BMD distribution and by pairwise spatial occurrence of BMD values using the gray-level co-occurrence (GLCM) approach. A linear multi-regression analysis (MultiReg) and a support vector regression algorithm with a linear kernel (SVRlin) were used to predict the FL from the image feature sets. The prediction performance was measured by the root mean square error (RMSE) for each image feature on independent test sets; in addition the coefficient of determination R² was calculated. The best prediction result was obtained with a GLCM feature set using SVRlin, which had the lowest prediction error (RSME = 1.040±0.143, R² = 0.544) and which was significantly lower that the standard approach of using BMD.mean and MultiReg (RSME = 1.093±0.133, R² = 0.490, p<0.0001). The combined sets including BMD.mean and GLCM features had a similar or slightly lower performance than using only GLCM features. The results indicate that the performance of high-dimensional BMD features extracted from MDCT images in predicting the biomechanical strength of proximal femur specimens can be significantly improved by using support vector regression.

Proceedings Article | 9 March 2011 Paper

Quantitative analysis of tumor matrix patterns through statistical and topological texture features

Mahesh Nagarajan, Xiaoxing Han, Markus Huber, Thomas Foster, Edward Brown, Axel Wismüller

Proceedings Volume 7965, 79651E (2011) https://doi.org/10.1117/12.878619

KEYWORDS: Tumors, Second-harmonic generation, Collagen, Feature extraction, Machine learning, Image classification, Fuzzy logic, Statistical analysis, Quantitative analysis, Harmonic generation

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Proceedings Article | 9 March 2011 Paper

Lesion classification on breast MRI through topological characterization of morphology over time

Mahesh Nagarajan, Markus Huber, Thomas Schlossbauer, Lawrence Ray, Andrzej Krol, Axel Wismüller

Proceedings Volume 7963, 79631U (2011) https://doi.org/10.1117/12.877742

KEYWORDS: Image classification, Magnetic resonance imaging, Breast, Feature extraction, Fuzzy logic, Statistical analysis, Binary data, Matrices, Data acquisition, Radiology

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Proceedings Article | 9 March 2011 Paper

Estimating local scaling properties for the classification of interstitial lung disease patterns

Markus Huber, Mahesh Nagarajan, Gerda Leinsinger, Lawrence Ray, Axel Wismueller

Proceedings Volume 7963, 79631Q (2011) https://doi.org/10.1117/12.878253

KEYWORDS: Lung, Image classification, Tissues, Computed tomography, CT reconstruction, Chest, Machine learning, Computer aided diagnosis and therapy, Seaborgium, Matrices

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Proceedings Article | 5 March 2011 Paper

Texture feature selection with relevance learning to classify interstitial lung disease patterns

Markus Huber, Kerstin Bunte, Mahesh Nagarajan, Michael Biehl, Lawrence Ray, Axel Wismueller

Proceedings Volume 7963, 796318 (2011) https://doi.org/10.1117/12.877894

KEYWORDS: Lung, Image classification, Prototyping, Distance measurement, Feature selection, Computed tomography, Stochastic processes, Quantization, Feature extraction, Matrices

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Proceedings Article | 9 March 2010 Paper

Analysis of breast lesions on contrast-enhanced magnetic resonance images using high-dimensional texture features

Mahesh Nagarajan, Markus Huber, Thomas Schlossbauer, Gerda Leinsinger, Axel Wismueller

Proceedings Volume 7624, 76241G (2010) https://doi.org/10.1117/12.844552

KEYWORDS: Statistical analysis, Breast, Magnetic resonance imaging, Fuzzy logic, Image classification, Feature extraction, Matrices, Error analysis, Magnetism, Yield improvement

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Proceedings Article | 9 March 2010 Paper

Prediction of biomechanical trabecular bone properties with geometric features using MR imaging

Markus Huber, Sarah Lancianese, Imoh Ikpot, Mahesh Nagarajan, Amy Lerner, Axel Wismüller

Proceedings Volume 7626, 76261O (2010) https://doi.org/10.1117/12.845022

KEYWORDS: Bone, Magnetic resonance imaging, In vivo imaging, Machine learning, Feature extraction, Tissues, Image analysis, Binary data, Magnetism, Neural networks

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Proceedings Article | 9 March 2010 Paper

Classification of interstitial lung disease patterns with topological texture features

Markus Huber, Mahesh Nagarajan, Gerda Leinsinger, Lawrence Ray, Axel Wismüller

Proceedings Volume 7624, 762410 (2010) https://doi.org/10.1117/12.844318

KEYWORDS: Lung, Image classification, Tissues, Computed tomography, Chest, Machine learning, Computer aided diagnosis and therapy, Statistical analysis, CT reconstruction, Matrices

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Showing 5 of 29 publications

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