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Bone. 2009 Sep;45(3):560-7. doi: 10.1016/j.bone.2009.04.250. Epub 2009 May 4.

Bone fracture risk estimation based on image similarity.

Author information

1
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA. wenjun.li@radiology.ucsf.edu

Abstract

We propose a fracture risk estimation technique based on image similarity. We employ image similarity indices to determine how images are similar to each other in their 3D bone mineral density distributions. Our premise for fracture risk estimation is that if a given scan is more similar to scans of subjects known to have fractures than to scans of control subjects, this subject is likely to have a higher degree of fracture risk. To test this hypothesis, we analyzed hip QCT scans of 37 patients with hip fractures and 38 age-matched controls. We divided the scans randomly into two groups: the Model Group and the Test Group. For each scan in the Test Group, the difference between the mean value of its image similarities to the Model fracture group and the mean value of its image similarities to the Model control group was used as index of fracture risk. We then used the estimated fracture risk indices to discriminate the fractured patients and controls in the Test Group. A test scan with a larger mean value of image similarities with respect to the Model fracture group was classified as a scan from a fractured patient, otherwise it was classified as a scan from a control subject. Based on ROC analysis, we compared the discrimination performances using image similarity measures with that obtained by using bone mineral density (BMD). When using BMD measured in the femoral neck, with the optimal BMD cutoff, the sensitivity and specificity were 86.5% and 73.7%. For the image similarity measures, the sensitivity ranged between 86.5% and 100%, and specificity ranged between 63.2% and 76.3%. By combining BMD with image similarity measures, the sensitivity and specificity reached 94.6% and 76.3% using linear discriminant analysis (LDA) algorithm, or 91.9% and 81.6% using recursive partitioning and regression trees (RPART) algorithm. In the RPART approach, the AUC value of the ROC curve was 0.923, higher than the AUC value of 0.835 when using BMD alone (p-value: 0.0046). Our results showed that combining BMD with image similarity measures resulted in improved hip fracture risk estimation.

PMID:
19414074
PMCID:
PMC2896043
DOI:
10.1016/j.bone.2009.04.250
[Indexed for MEDLINE]
Free PMC Article

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