Authors : T. Kalpalatha Reddy and N. Kumaravel
Abstract: Bone architecture is an important factor that determines bone strength in addition to bone mass. Texture analysis of the trabecular bone pattern on axial dental CT is being investigated as a potential means to characterize the bone quality. In this study, we examined the use of an artificial neural network and features from different scales of curvelet transform analysis to obtain a measure related to bone architecture and quality. Texture features are extracted from 220 image regions of jaw bone CT images (both male and female) using spatial gray level dependence method, run length, histogram and curvelet transform. By using the Neural Network Classifier, the classification of bone samples at different locations of the jawbone region is performed. First the combination of the features from run length and first order statistics achieved overall classification accuracy ≥69.23%. Features selected from the curvelet based cooccurrence matrix performed better with overall classification accuracy >80%. In order to increase the success rate the classification is done using the combination of curvelet statistical features, run length and curvelet cooccurence features as feature vector and using this, a mean success rate of 97.2% is obtained.
T. Kalpalatha Reddy and N. Kumaravel, 2009. Texture Analysis of Bone CT Images for Classification and Characterization of Bone Quality. International Journal of Soft Computing, 4: 223-228.