Dental Cone Beam Computed Tomography for Trabecular Bone Quality Analysis in Maxilla and Mandible

Dental Cone Beam Computed Tomography for Trabecular Bone Quality Analysis in Maxilla and Mandible

T. Christy Bobby (M. S. Ramaiah University of Applied Sciences, India), Shwetha V. (M. S. Ramaiah University of Applied Sciences, India) and Vijaya Madhavi (East Point College of Engineering and Technology, India)
Copyright: © 2019 |Pages: 27
DOI: 10.4018/978-1-5225-6243-6.ch007

Abstract

The stability of a dental implant is one of the most important aspects that decide the success rate of implant treatment. The stability is considerably affected by the strength of trabecular bone present in maxilla and mandible. Thus, finding of trabecular bone strength is a key component for the success of dental implants. The trabecular bone strength is usually assessed by quantity of bone in terms of bone mineral density (BMD). Recently, it has been revealed that along with quantity of bone, strength of the bone also depends on quality features commonly referred as trabecular bone microarchitecture. Since the quality of the trabecular bone is varying across the maxilla and mandible, preoperative assessment of trabecular bone microarchitecture at sub-region of maxilla and mandible are essential for stable implant treatment. Thus, in this chapter, the authors inscribe the quantitative analysis of trabecular bone quality in maxilla and mandible using CBCT images by employing contourlet transform.
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Introduction

Noninvasive imaging modalities play a vital role in dental implant analysis such as preoperative, intraoperative and postoperative assessments. In recent years, preoperative diagnosis and treatment plan of implant has been done using Cone Beam Computed Tomography (CBCT). Preoperatively, the quantity and quality of trabecular bone can be calculated from CBCT images for the placement of implant in appropriate position (Shah, Bansal & Logani, 2014). The CBCT imaging system records the images based on a cone-shaped X-ray beam positioned on a 2-D detector. When Compared to conventional MultiSlice CT (MSCT), dental CBCT possess several advantages such as lower radiation doses, fast scanning time, acceptable image resolution (80 to 400 μm) and affordable cost (Ho et al., 2013).

In medical image analysis, surface properties of image are typically analyzed using spatial and spectral domain analysis (Boehm, Lutz, Korner & Mutschler, 2009; Gregory, Stewart, Undrill, Reid & Aspden, 2004; Bullmore et al. 2004, De ́fossez et al., 2003). Contourlet transform is a spectral content analysis with distinctive features such as multiresolution exploration and time-frequency localization. Also it explores directionality and anisotropy information of an image. It is a two dimensional multiscale transform, utilizing fast-iterated directional filter bank algorithm, which captures geometrical and directional information by means of multi-resolution and multi-direction expansion. It can look out for smooth lines, edges, contours and curves and iterated filter banks making it computationally efficient. Hence the resultant transform co-efficient contains multi-resolution, local and directional information of an image. The multiresolution representation allows image to be approximated from coarse to fine resolution in a successive manner. By time-frequency localization the elements of image such as points, lines and curves are localized both in frequency and spatial domains. The directionality feature represents the orientation information of the basic elements oriented at several directions in an image. Anisotropy represents basis elements of elongated shapes with various aspect ratios.

In double filter bank, initially Laplacian Pyramid (LP) is used to capture point discontinuities followed by directional filter banks to link point discontinuities to linear structures. The number of directions are doubled at all finer scales. Hence the final result is image expansion using contour segments and is named as contourlets (Do & Vetterli, 2005). Contourlet transform is used in many medical and other applications due to its simple discrete construction, low complexity and redundancy. Thus Contourlet transform is applied for efficient representation of medical images with contours oriented in different directions (Katsigiannis, Keramidas & Maroulis, 2010).

It is clearly evident from the CBCT images of maxilla and mandible that the trabecular bone structures are represented as curves and straight lines oriented at many directions. Thus in this book chapter I am planning to inscribe about the quantitative analysis of trabecular bone quality in maxilla and mandible using CBCT images by employing contourlet transform.

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