Dr. Markus B. Huber Profile

Dr. Markus B. Huber

Research Scholar at Univ of Rochester Medical Ctr

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Author

Publications (15)

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 | 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

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

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

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 | 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

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 | 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 | 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 15 publications

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