Predicting the Biomechanical Strength of Proximal Femur Specimens with Minkowski Functionals and Support Vector Regression.

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Predicting the Biomechanical Strength of Proximal Femur Specimens with Minkowski Functionals and Support Vector Regression.

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2014 nî lūn-bûn

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2014年の論文

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2014年学术文章

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2014年学术文章

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2014年学术文章

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2014年学术文章

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2014年学术文章

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2014年學術文章

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2014年學術文章

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2014年學術文章

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name

Predicting the Biomechanical S ...... and Support Vector Regression.

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Predicting the Biomechanical S ...... and Support Vector Regression.

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Item

label

Predicting the Biomechanical S ...... and Support Vector Regression.

@en

Predicting the Biomechanical S ...... and Support Vector Regression.

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Predicting the Biomechanical S ...... and Support Vector Regression.

@en

Predicting the Biomechanical S ...... and Support Vector Regression.

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P1476

Predicting the Biomechanical S ...... and Support Vector Regression.

@en

P2093

Axel Wismüller

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Chien-Chun Yang

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Eva-Maria Lochmüller

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Felix Eckstein

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Jan S Bauer

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Julio Carballido-Gamio

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Mahesh B Nagarajan

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Markus B Huber

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Thomas M Link

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P2860

Improved performance of hip DXA using a novel region of interest in the upper part of the femoral neck: in vitro study using bone strength as a standard of reference.

Structural analysis of trabecular bone of the proximal femur using multislice computed tomography: a comparison with dual X-ray absorptiometry for predicting biomechanical strength in vitro.

Proximal femur specimens: automated 3D trabecular bone mineral density analysis at multidetector CT--correlation with biomechanical strength measurement.

Automated 3D trabecular bone structure analysis of the proximal femur--prediction of biomechanical strength by CT and DXA.

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

Computer-aided diagnosis in phase contrast imaging X-ray computed tomography for quantitative characterization of ex vivo human patellar cartilage

Assessment of fracture risk.

Classification of Small Lesions in Breast MRI: Evaluating The Role of Dynamically Extracted Texture Features Through Feature Selection.

Classification of small lesions in dynamic breast MRI: Eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement over time

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P31

scholarly article

P356

10.1117/12.2041782

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P478

9038

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P50

Thomas Baum

P577

2014-03-01T00:00:00Z

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P698

29170582

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P921

biomechanics

support vector machine

P932

5697789

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