Abstract

Three-dimensional reconstruction plays a vital role in assisting doctors and surgeons in diagnosing the healing progress of bone defects. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, this study omits the volume rendering methods. Many improvements have been made to surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images and studying the effects of different parameter settings for the improvements. Hence, this study attempts near real-time surface rendering for large medical images. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 5 and mesh simplification reduction factor of 0.1 is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61%, respectively.

Highlights

  • Bone void fillers are used to treat bone voids caused by various reasons

  • For the table results in this chapter, MC refers to Marching Cubes, MT refers to Marching Tetrahedra, S refers to convolution kernel size for smooth3, and RF refers to the reduction factor for reducepatch

  • Reconstruction time, rendering time, and the number of vertices and faces are not compared here as the reconstruction accuracy is given the highest emphasis in this comparison study

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Summary

Introduction

Doctors and surgeons examine bone defects implanted with a bone void filler by scanning it through a computed tomography (CT) scanner, which results in a stack of two-dimensional (2D) CT images. It is difficult to judge the healing progress of the bone defect as there are noises and artefacts present in the 2D CT bone defect images, which obscure the details of the bone defect. It is difficult to visualize the bone defect as a whole with the 2D CT image stack. By visualizing the 2D CT image stack as a three-dimensional (3D) view, 3D models can help doctors increase the quality of experience and diagnosis accuracy [1,2]. The process of visualizing the 2D CT image stack as a 3D view is called 3D reconstruction

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