An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Using SOM and PCA for analysing and interpreting data from a P-removal SBR
Engineering Applications of Artificial Intelligence
The curvelet transform for image denoising
IEEE Transactions on Image Processing
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In this work, directional multiresolution curvelet transform is performed in radiographic images to characterize the trabecular structure. The trabecular regions of normal and abnormal human femur bone images are used for the study. The regions of interest such as femoral neck and head are analyzed and compared. The curvelet coefficients are calculated based on each scale and orientation for trabecular images. The mean and energy of the curvelet coefficients associated with each subband are computed. These values are used as the texture feature vector elements to evaluate changes taking place in the trabecular architecture. The three most significant mean and energy feature vector are found using principal component analysis and these values are used as an input to the Adaboost classifier. The results show that the architectural variations are more in the femoral neck when compared to femoral head. AdaBoost classifier performs better in terms of sensitivity (90%) and specificity (100%) for the chosen parameters for femoral neck region when compared to head regions.