Three-Dimensional Reconstruction of Cone Beam Computed Tomography Using Splines Interpolation Technique for Dental Application1 (2016)

Dentistry deals with diagnosis and treatment of diseases in the oral cavity; dental radiography is a necessary tool for imaging the oral cavity as well as the maxillofacial region. The advancement of imaging technology in medicine has developed a method to visualize the volumetric information contained in a two-dimensional (2D) image. Cone beam computed tomography (CBCT) is usually used for imaging the maxillofacial region, for it provides effective information of the bone site. Dentist requires a 3D model of the maxillofacial region for both diagnosis and treatment. For treatment planning, the site must be imaged clearly so that they can visualize the volumetric information. CBCT imaging has more benefit than conventional imaging system [1,2]. Since it provides volumetric information of the site and also has lower radiation exposure, the physicians prefer CBCT imaging method. The proposed method for 3D model of teeth is to extract the region of interest from each slice of image using morphological operation and segmentation using thresholding. The segmented area is then arranged on a grid, and the surface is interpolated to the nearest neighborhood. This forms the 3D model of the segmented teeth.Three-dimensional information of the maxillofacial region aids the clinicians in orthodontic implant planning and surgery. The 3D model will provide more information regarding the size, width, and depth of the implant and also proper treatment planning for the pathological conditions related to the maxillofacial region. Moreover, simple visualization of the teeth will help clinicians to solve the potential oral problems. Figure 1(a) is a panoramic view of maxillofacial region with dental complication, and Fig. 1(b) shows the CBCT image. The panoramic image helps in visualizing 2D information where the clinician analyzes the location and arrangement of teeth but the temporal and structural information can be visualized by 3D modeling of the maxillofacial region.The acquired CBCT image slice is selected for morphological operation. Each slice has anatomical structures of the maxillofacial region with different gray level. Hard tissues have higher intensity while soft tissues have lower intensity. Boundaries of teeth in the image slice are extracted spatially using morphological operation with the help of specific structuring element. To obtain boundary, erosion operation is performed on the image slice, and then, it is subtracted from the original image [3,4]. The segmentation is done by applying thresholding. The segmented region is taken and plotted on the grid; neighborhood points on the grid are interconnected by splines interpolation [5] and the surface is patched on it; this constructs 3D model [6] of the teeth.CBCT image slice is processed for boundary extraction through morphological operation. The grayscale image slice contains morphological structure of the teeth with other soft tissues represented in lower intensity. The boundary extraction is performed spatially by erosion and subtraction of eroded image with the original image. Other gradient operators are available for boundary extraction, but the morphological boundary extraction is found to be advantageous for our application as the image contains nonhomogeneous structure. Structuring element plays an important role in morphological operation. A suitable nonisotropic structuring element is chosen for morphological operation. The erosion operation is performed on the image slice spatially. The original image is taken and subtracted from the eroded image, and the boundaries of all the structures in the image are obtained. The resultant image has boundary information of structure as the common intensity in both the images is removed by subtraction, leaving only the boundary detail of each structure.The extracted boundary of image slice has both hard tissue (teeth) and soft tissue. To extract the teeth region, segmentation is used. Segmentation of teeth from the image slice is achieved by thresholding operation. Thresholding operation is used to remove the unwanted region in the image slice. The histogram is plotted for the image and the threshold intensity value is chosen from it. The chosen intensity is considered as margin to remove the unwanted region. The image obtained after thresholding operation leaves only the teeth structure and removes the other regions. Further erosion operation is performed to obtain the thin edge of segmented image.Region segmentation is followed by surface interpolation for 3D modeling. The entire segmented image slice is arranged in sequential order and plotted on the coordinate system. Further, a mesh grid is introduced over the plotted sample points. The mesh grid provides a volumetric structure for the 3D model that is to be constructed where each point in grid is connected with the neighborhood sample points in the grid. Sample points represent the coordinates of the structure in the image; it is chosen for interpolation. Splines interpolation technique is used to interpolate the distance of sample points. Once the distance and path of the points are calculated, they are joined on the grid to get the surface structure.The volume rendering requires surface information and structure of the model. After the interpolation, the interpolated surface of the teeth model has structural information, and this structure is patched with the smooth surface. This process is continued in all directions to obtain the volume rendering. Adding patch to the surface involves unique methodology to obtain the smooth and clear model of the object. The obtained model has a continuous surface as all the sample points are linearly interpolated with its neighborhood. The complete process of 3D modeling is represented in the flowchart (Fig. 2). The proposed approach is implemented on matlab r2015b, and the average computation time of our algorithm is 2 min on Intel(R) Core(TM) i5-2400 CPU, clock of 3.1 GHz, and 4 GB RAM memory.For experimental purpose, we used a CBCT slice of maxillofacial region for 3D modeling. From each slice, the teeth area was segmented by the proposed method. Clear contour information was obtained by thresholding. All the sample points were interconnected in mesh grid, and all discontinuous portions were connected using splines interpolation function. Finally, the 3D model of the sample object was reconstructed successfully with high resolution and smooth surface (Fig. 3). The surface of the object depends on the contour edge that is obtained through segmentation; accuracy of the contour region exacts the 3D model.The 3D model of the teeth is reconstructed successfully using splines interpolation technique. The resolution of the 3D model of teeth depends on the number of slices that are used to reconstruct. For the purpose of testing, we have taken only a few slices. Segmentation also affects the surface phenomenon of the 3D model. The thresholding operation allows proper segmentation of the hard tissue through which the 3D model is constructed. By this approach, we were able to reconstruct the outer structure of the teeth. To obtain inner structure like cavity and nerves that pass through, high-resolution image and improved segmentation methods are required. The proposed system has good resolution of the 3D modeled teeth, and the outer surfaces of the teeth are clearly visible.