Dr. Julio Carballido-Gamio Profile

Dr. Julio Carballido-Gamio

at Univ of California San Francisco

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Publications (6)

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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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 | 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 | 26 March 2008 Paper

Three-dimensional image registration of MR proximal femur images for the analysis of trabecular bone parameters

Janet Blumenfeld, Colin Studholme, Julio Carballido-Gamio, Thomas Link, Sharmila Majumdar

Proceedings Volume 6914, 69140G (2008) https://doi.org/10.1117/12.770460

KEYWORDS: Bone, Image registration, Magnetic resonance imaging, Image resolution, 3D image processing, Image analysis, Image segmentation, Medical imaging, Image processing, In vivo imaging

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Proceedings Article | 12 May 2004 Paper

Application of a fuzzy inference system to the quantification of 3D magnetic resonance imaging of breast tissue

Julio Carballido-Gamio, Catherine Klifa, Sharmila Majumdar, Nola Hylton

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.536238

KEYWORDS: Breast, Tissues, Image segmentation, Magnetic resonance imaging, Fuzzy systems, Breast cancer, Mammography, 3D image processing, Imaging systems, Fuzzy logic

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Showing 5 of 6 publications

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