| Autor: |
Mahesh B. Nagarajan, Thomas M. Link, Thomas Baum, Chien-Chun Yang, Felix Eckstein, Julio Carballido-Gamio, Jan S. Bauer, Axel Wismüller, Eva Lochmüller, Markus B. Huber, Sharmila Majumdar |
| Rok vydání: |
2012 |
| Předmět: |
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| Zdroj: |
Medical Imaging: Computer-Aided Diagnosis |
| ISSN: |
0277-786X |
| DOI: |
10.1117/12.911402 |
| Popis: |
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 R 2 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, R 2 = 0.544) and which was significantly lower that the standard approach of using BMD.mean and MultiReg (RSME = 1.093±0.133, R 2 = 0.490, p |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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