The evaluation of jaw bone trabecular structure and quality could be useful for characterization and response of the bone for dental implants. Current clinical methods for assessment of bone quality at the implant sites largely depend on assessing bone mineral density using Dual energy X-ray absorptionometry. However, this does not provide any information about bone structure which is considered to be an equally important factor in assessing bone quality. This paper presents a novel approach for computer analysis of trabecular (or cancellous) bone structure using multiresolution based texture analysis to evaluate changes taking place in the architecture of bone with age and gender. The findings are compared with Hounsfield Units measured from the CT machine at different sites, which is a standard reference. Fifty patients were subjected to clinical CT to obtain the CT number and texture based architectural parameters respectively. In each site texture features were extracted using gray level co-occurrence matrices (GLCM), Run length matrices, Histogram and curvelet based statistical & co occurrence analysis. A very difficult problem in classification techniques is the choice of features to distinguish between classes. However the performance of any classifier is not optimized when all features are used. The feature optimization problem is addressed using Principle component analysis in terms of the best recognition rate and the optimal number of features. Testing this on a series of 120 image sections of trabecular bone with normal, partial and total edentulous patients correctly classified over 90% of the porous bone group with an overall accuracy of 87.8%-95.2%.The results shows that by using the Classification & Regression Tree approach the combination of the features from gray level and Ist order statistics achieved overall classification accuracy in the range of 87.8-90.24%. Features selected from the curvelet based co occurrence matrix performed better with overall classification accuracy of 92.89%.In order to increase the success rate the classification is done using the combination of curvelet statistical features and curvelet co occurrence features as feature vector and using this, a mean success rate of 95.2% is obtained.

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<p>Accurate classification of dental caries is crucial for effective oral healthcare. Filters help to increase exposure of the picture taken for the investigation without degrading image quality. Selective median filter is the chosen preprocessing technique that helps to reduce the noise present in the captured image. Dental caries classification system is a model used to detect the presence of cavity in the given input image. Dental caries classification system is evolved with the use of conventional techniques to artificial neural network. Deep learning models are the artificial neural network models that can able to learn the features from the raw images available in the dataset. If this raw image has noise, then it severely affects the accuracy of the deep learning models. In this paper, impact of the preprocessing technique on the classification accuracy is analyzed. Initially, raw images are taken for training on deep learning models without applying any preprocessing technique. This study investigates the impact of Selective median filtering on a dental caries classification system using deep learning models. The motivation behind this research is to enhance the accuracy and reliability of dental caries diagnosis by reducing noise, removing artifacts, and preserving important details in dental radiographs. Experimental results demonstrate that the implementation of Selective median filtering significantly improves the performance of the deep learning model. The hybrid neural network (HNN) classifier achieves an accuracy of 96.15% with Selective median filtering, outperforming the accuracy of 85.07% without preprocessing. The study highlights the theoretical contribution of Selective median filtering in enhancing dental caries classification systems and emphasizes the practical implications for dental clinics, offering improved diagnostic capabilities and better patient outcomes.</p>

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