Parallel thinning with two-subiteration algorithms
Communications of the ACM
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to variable and feature selection
The Journal of Machine Learning Research
A simple method for fitting of bounding rectangle to closed regions
Pattern Recognition
Isthmus-based 6-directional parallel thinning algorithms
DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
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Background: Lifespan and its quality can be improved by early diagnosis of osteoporosis. Analysis of trabecular boundness on digital hip radiographs could be useful for identifying subjects with low bone mineral density (BMD) or osteoporosis. The main aim of our study was to evaluate the ability of a kernel-based support vector machine (SVM) with respect to diagnosis and add to knowledge about the trabecular features of digital hip radiographs for identifying subjects with low BMD. Method: In this paper we present an SVM kernel classifier-based computer-aided diagnosis (CAD) system for osteoporotic risk detection using digital hip radiographs. Initially, the original radiograph was intensified, then trabecular features such as boundness, orientation, solidity of spur and delta were evaluated and radial bias function (RBF) based discrimination was manifested. The next step was the evaluation of the diagnostic capability of the proposed method in order to spot subjects with low BMD at the femoral neck in 50 (50.7+/-14.3 years) South Indian women with no previous history of osteoporotic fracture. Out of 50 subjects, 28 were used to train the classifier and the other 22 were used for testing. Results: The proposed system has achieved the highest classification accuracy documented so far by means of a fivefold cross-validation analysis with mean accuracy of 90% (95% confidence interval (CI): 82 to 98%); sensitivity and positive predictive value (PPV) were 90% (95% CI: 82 to 98%) and 89% (95% CI: 81 to 97%), respectively. Pearson's correlation was observed at the level of p