Segmentation and Classification of Jaw Bone CT images using Curvelet based Texture features

Abstract

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.

Automatic Segmentation of Mandibular Cortical Bone on Cone-Beam CT Images Based on Histogram Thresholding and Polynomial Fitting

“…Specificity or true negative rate measures the percentage of the correctly classified background pixels, which formula is written in Eq. (6). The sensitivity value is not measured in the slices that contain no segmentation object.…”

“…Meanwhile, the analysis of bone quality and quantity in CBCT image is of major importance for the success of implant placement. The amount of cortical bone is responsible for the primary stability of the implant [6]. Therefore, accurate segmentation of mandibular cortical bone becomes a necessity.…”

DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease

“…A more advanced approach is utilizing Curvelet Transform (CT) as a feature extraction method for its ability to obtain both linear and curved edges along multiple scales and orientations [42]. In this regard, several studies have applied CT in various computer vision tasks, namely tumor detection [43], [44], image segmentation [45], [46], [47], signature verification [48], [49], and face recognition [50], [51], [52]. However, despite its advantages, limited number of studies have reported using CT as a feature extraction tool for AD detection using MRI images [53], [54].…”