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ABSTRACT: Although the areal Bone Mineral Density (BMD) measurements from dual-energy X-ray absorptiometry (DXA) are able to discriminate between hip fracture cases and controls, the femoral strength is largely determined by the 3D bone structure. In a previous work a statistical model was presented which parameterizes the 3D shape and BMD distribution of the proximal femur. In this study the parameter values resulting from the registration of the model onto DXA images are evaluated for their hip fracture discrimination ability with respect to regular DXA derived areal BMD measurements. The statistical model was constructed from a large database of QCT scans of females with an average age of 67.8±17.0years. This model was subsequently registered onto the DXA images of a fracture and control group. The fracture group consisted of 175 female patients with an average age of 66.4±9.9years who suffered a fracture on the contra lateral femur. The control group consisted of 175 female subjects with an average age of 65.3±10.0years and no fracture history. The discrimination ability of the resulting model parameter values, as well as the areal BMD measurements extracted from the DXA images were evaluated using a logistic regression analysis. The area under the receiver operating curve (AUC) of the combined model parameters and areal BMD values was 0.840 (95% CI 0.799-0.881), whilst using only the areal BMD values resulted in an AUC of 0.802 (95% CI 0.757-0.848). These results indicate that the discrimination ability of the areal BMD values is improved by supplementing them with the model parameter values, which give a more complete representation of the subject specific shape and internal bone distribution. Thus, the presented method potentially allows for an improved hip fracture risk estimation whilst maintaining DXA as the current standard modality.
Bone 08/2012; 51(5):896-901. · 4.02 Impact Factor
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Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
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Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011 - 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part III; 01/2011
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ABSTRACT: Though graph cut based segmentation is a widely-used technique, it is known that segmentation of a thin, elongated structure is challenging due to the "shrinking problem". On the other hand, many segmentation targets in medical image analysis have such thin structures. Therefore, the conventional graph cut method is not suitable to be applied to them. In this study, we developed a graph cut segmentation method with novel Riemannian metrics. The Riemannian metrics are determined from the given "initial contour," so that any level-set surface of the distance transformation of the contour has the same surface area in the Riemannian space. This will ensure that any shape similar to the initial contour will not be affected by the shrinking problem. The method was evaluated with clinical CT datasets and showed a fair result in segmenting vertebral bones.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 3):554-61.
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ABSTRACT: This work presents a statistical model of both the shape and Bone Mineral Density (BMD) distribution of the proximal femur for fracture risk assessment. The shape and density model was built from a dataset of Quantitative Computed Tomography scans of fracture patients and a control group. Principal Component Analysis and Horn's parallel analysis were used to reduce the dimensionality of the shape and density model to the main modes of variation. The input data was then used to analyze the model parameters for the optimal separation between the fracture and control group. Feature selection using the Fisher criterion determined the parameters with the best class separation, which were used in Fisher Linear Discriminant Analysis to find the direction in the parameter space that best separates the fracture and control group. This resulted in a Fisher criterion value of 6.70, while analyzing the Dual-energy X-ray Absorptiometry derived femur neck areal BMD of the same subjects resulted in a Fisher criterion value of 0.98. This indicates that a fracture risk estimation approach based on the presented model might improve upon the current standard clinical practice.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 2):393-400.
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ABSTRACT: The objectives of this study were to perform a clinical study analyzing bone quality in multidetector computed tomographic images of the femur using bone mineral density (BMD), cortical thickness, and texture algorithms in differentiating osteoporotic fracture and control subjects; to differentiate fracture types.
Femoral head, trochanteric, intertrochanteric, and upper and lower neck were segmented (fracture, n = 30; control, n = 10). Cortical thickness, BMD, and texture analysis were obtained using co-occurrence matrices, Minkowski dimension, and functional and scaling index method.
Bone mineral density and cortical thickness performed best in the neck region, and texture measures performed best in the trochanter. Only cortical thickness and texture measures differentiated femoral neck and intertrochanteric fractures.
This study demonstrates that differentiation of osteoporotic fracture subjects and controls is achieved with texture measures, cortical thickness, and BMD; however, performance is region specific.
Journal of computer assisted tomography 10/2010; 34(6):949-57. · 1.38 Impact Factor
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ABSTRACT: The favored treatment for many hip fractures is a sliding hip screw, and its usage is expected to increase in the future. Failures can be reduced, and complications detected earlier by semi-automated CT image analysis. The most frequent failure is due to the screw cut-out from the femoral head.
An image-based method was developed for early detection of complications and assessment of anchorage quality relative to implant model, bone quality or tip-apex distance (TAD). This method evaluates micro-migration using CT images acquired at different time points (immediately post-op and 3-month later). Serial CT image registration and transformation methods were applied, including point-based registration, to achieve semi-automated evaluations.
Qualitative and quantitative validation of the image registration was performed with measurement mean error determination by different observers. The micro-migration evaluation by clinicians compared favorably with semi-automated image-based results.
Semi-automatic evaluation of hip screw micro-migration using CT images is feasible and can aid observation of convalescence. The method may be amenable to full automation, a future goal for this work.
International Journal of Computer Assisted Radiology and Surgery 09/2010; 5(5):455-60. · 1.48 Impact Factor
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ABSTRACT: Standard diagnostic techniques to quantify bone mineral density (BMD) include dual-energy x-ray absorptiometry (DXA) and quantitative computed tomography. However, BMD alone is not sufficient to predict the fracture risk for an individual patient. Therefore, the development of tools, which can assess the bone quality in order to predict individual biomechanics of a bone, would mean a significant improvement for the prevention of fragility fractures. In this study, a new approach to predict the fracture risk of proximal femora using a statistical appearance model will be presented.
100 CT data sets of human femur cadaver specimens are used to create statistical appearance models for the prediction of the individual fracture load (FL). Calculating these models offers the possibility to use information about the inner structure of the proximal femur, as well as geometric properties of the femoral bone for FL prediction. By applying principal component analysis, statistical models have been calculated in different regions of interest. For each of these models, the individual model parameters for each single data set were calculated and used as predictor variables in a multilinear regression model. By this means, the best working region of interest for the prediction of FL was identified. The accuracy of the FL prediction was evaluated by using a leave-one-out cross validation scheme. Performance of DXA in predicting FL was used as a standard of comparison.
The results of the evaluative tests demonstrate that significantly better results for FL prediction can be achieved by using the proposed model-based approach (R = 0.91) than using DXA-BMD (R = 0.81) for the prediction of fracture load.
The results of the evaluation show that the presented model-based approach is very promising and also comparable to studies that partly used higher image resolutions for bone quality assessment and fracture risk prediction.
Medical Physics 06/2010; 37(6):2560-71. · 2.83 Impact Factor
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Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands, 14-17 April, 2010; 01/2010
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ABSTRACT: For planning surgical interventions at the spine affected by osteoporosis, accurate information about the local bone quality in terms of anchorage strength for implants is very important. Based on previous work on automated bone quality assessment on the proximal femur with a completely automated model-based approach, this paper describes first applications and results on the lumbar vertebrae.
As basis for the analysis, CT datasets of 17 spinal specimens, with a resolution of 0.7 mm x 0.7 mm x 0.7 mm have been used. A combined statistical model of 3D shape and intensity value distribution was created for these datasets and used to predict the measured bone mineral density (BMD). Different regions of interest were tested, model parameters with high correlation with BMD were identified. Leave-one-out tests were performed to evaluate the capability for the BMD-prediction using regression models.
High correlation values (R = 0.94) between measured and predicted BMD were achieved and the high predictive quality of the model could be shown.
Although the results are only valid for an insufficient small sample size of specimen data, they show a clear potential for clinical application. Therefore, work in the future will focus on clinical validation with larger sample size and the inclusion of biomechanical properties in addition to BMD.
International Journal of Computer Assisted Radiology and Surgery 05/2009; 4(3):239-43. · 1.48 Impact Factor
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IEEE Trans. Med. Imaging. 01/2009; 28:1560-1575.
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ABSTRACT: A solid and accurate proximal femur segmentation technique using the popular active shape model (ASM) is proposed. For generating an optimal shape prior, the minimum description length, based on 200 supervised manual segmented proximal femur shapes, is used. The segmentation is based on a coarse to fine scaling technique including a profile scale space method. The segmentation results are compared using an optimal defined initial pose and a pose based on a registration technique. Using ideal template initialization, 95% of the shapes have been recovered exactly (average point-to-point error approximately 13 pixels, average point-to-boundary error approximately 7 pixels). Using a template-based initialization based on a registration technique, a successful segmentation rate of approximately 89% is achieved, with an average point-to-point error approximately 12 pixels, and an average point-to-boundary error approximately 8 pixels. With an adequate template initialization and an improved ASM, this method seems to provide an accurate tool for segmentation of the proximal femur shapes on conventional hip overview x-ray images.
Medical Physics 06/2008; 35(6):2463-72. · 2.83 Impact Factor
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ABSTRACT: In this paper we present a knowledge-based femur detection algorithm. The algorithm uses femur corpus constraints, Canny edge detection and Hough lines. For optimal femur template placement in the local area we use cross-correlation. The segmentation itself is done with an optimized active shape modeling technique. Using the knowledge-based technique we have located 95% of the femur shapes of N=117 X-rays. From those 83% of the target femur shapes have been segmented successfully (point-to-point error: approximately 14 pixels, point-to-boundary error = approximately 9 pixels).
Computers in Biology and Medicine 06/2008; 38(5):535-44. · 1.09 Impact Factor
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ABSTRACT: Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2008; 11(Pt 1):568-75.
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Karl Fritscher,
Benedikt Schuler,
Thomas Link,
Felix Eckstein,
Norbert Suhm,
Markus Hänni,
Clemens Hengg, Rainer Schubert,
Rainer Schubert}@umit,
At,
Clemens Hengg@uki At
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ABSTRACT: Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. How-ever, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the pro-posed model based approach is capable of predicting two different biomechani-cal parameters accurately and fully automatically in two different testing scenarios.
LNCS. 01/2008; 5241:568-575.
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ABSTRACT: Nowadays clinical diagnostic techniques like e.g. dual-energy X-ray absorptiometry are used to quantify bone quality. However, bone mineral density alone is not sufficient to predict biomechanical properties like the fracture load for an individual patient. Therefore, the development of tools, which can assess the bone quality in order to predicting individual biomechanics of a bone, would mean a significant improvement for the prevention of fractures. In this paper an approach to predict the fracture load of proximal femora by using a statistical appearance model will be presented. For this purpose, 96 CT-datasets of anatomical specimen of human femora are used to create statistical models for the prediction of the individual fracture load. Calculating statistical appearance models in different regions of interest by using principal component analysis (PCA) makes it possible to use geometric as well as structural information about the proximal femur. By regressing the output of PCA against the individual fracture load of 96 femora multi-linear regression models using a leave-one-out cross validation scheme have been created. The resulting correlations are comparable to studies that partly use higher image resolutions.
SPIE Medical Imaging; 01/2008
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Bildverarbeitung für die Medizin 2008, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 6. bis 8. April 2008 in Berlin; 01/2008
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Bildverarbeitung für die Medizin 2008, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 6. bis 8. April 2008 in Berlin; 01/2008
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Bildverarbeitung für die Medizin 2008, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 6. bis 8. April 2008 in Berlin; 01/2008
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Data Knowl. Eng. 01/2007; 62:308-326.
