论文阅读:Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using wat

【论文信息】

Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation
Dentomaxillofacial Radiology 2015 IF 1.9

【背景】

现在CBCT的分辨率大约为0.1mm;MSCT的在0.2mm以上。而牙齿根部的牙周膜宽度小于0.1mm,所以在医学影像中很难看到。而有些方式可以提高分辨率,但同时也会提高放射性物质的摄入,临床上很受限制。而且分辨率越高,噪声越多。
CBCT虽然分辨率比MSCT高,但是对比度较低。所以还是MSCT容易分割,尽管分割结果的三维形状会有较大变形。
大部分商用软件都是针对MSCT。CBCT专用的很少很少。
提到【8,9】牙齿分割,【10】牙齿分类有前途。

【方法】

分为五步

1 数据获取,CT扫描

数据的获取。

2 预处理

一个是通过滤镜来加强对比度,比如mimics中的非线性滤波。
还有个是ROI的选取,可以加速处理过程,不必对全部的体素计算。
要尽量在张嘴模式扫描,不然上下颌的连接处理起来很麻烦。
这里写图片描述

3 粗分割

这一步要把牙齿,颌骨,软组织互相分离开。最后要得到两个模型:一个是ROI中带牙根牙冠的整个牙齿模型(没有颌骨

### High-speed and high-efficiency 3D shape measurement using Gray-coded light techniques Three-dimensional (3D) shape measurement has become an essential technique in various fields, including industrial inspection, robotics, and biomedical imaging. One of the most promising methods for achieving high-speed and high-efficiency 3D shape measurement is the use of Gray-coded light techniques. Gray-coded light techniques rely on structured light projection, where a series of binary patterns encoded with Gray codes are projected onto the surface of an object. The reflected or deformed patterns are then captured by a camera, and the 3D shape of the object is reconstructed based on the geometric relationship between the projector and the camera. Gray codes are particularly useful in this context because they minimize the risk of misinterpretation during the decoding process, ensuring that only one bit changes between consecutive codes. This property reduces the likelihood of errors in the phase unwrapping process, which is critical for accurate 3D reconstruction. The use of Gray-coded light enables high-speed acquisition due to the binary nature of the projected patterns. Binary patterns can be displayed at high frame rates using digital micromirror devices (DMDs), allowing for rapid data collection. Additionally, the efficiency of the method is enhanced by reducing the number of required patterns compared to other encoding strategies, such as sinusoidal phase-shifting methods. For instance, a complete 3D shape measurement using Gray-coded light may require only a fraction of the time needed for a single spectrum acquisition using conventional methods, especially when combined with fast decoding algorithms [^1]. Moreover, the integration of Gray-coded light techniques with high-resolution imaging systems can provide detailed morphological and surface information. This fusion of high-speed data acquisition and high-spatial resolution contributes significantly to the overall quality and fidelity of the 3D reconstruction. In some cases, the system may also benefit from a broader spectral range (UV-IR), further expanding its applicability across different materials and environments [^1]. To evaluate the performance of 3D shape measurement techniques, metrics such as Peak Signal-to-Noise Ratio (PSNR) are often employed. PSNR quantifies the difference between the original and reconstructed surfaces, serving as an indicator of the visual quality and accuracy of the measurement. Higher PSNR values generally correspond to better reconstruction quality, as demonstrated in comparative studies where certain techniques achieved significantly higher PSNR values due to improved contrast and suitability for specific types of surfaces [^2]. ### Code Example: Gray Code Generation for 3D Reconstruction The following Python code demonstrates how to generate Gray codes for a given number of bits, which can be used in structured light projection for 3D shape measurement: ```python def gray_code(n): """Generate Gray codes for n bits.""" if n <= 0: return [] if n == 1: return ['0', '1'] prev = gray_code(n - 1) return ['0' + code for code in prev] + ['1' + code for code in reversed(prev)] # Example usage n_bits = 4 gray_patterns = gray_code(n_bits) for pattern in gray_patterns: print(pattern) ``` This function recursively generates Gray codes, ensuring that each successive code differs by only one bit, which is crucial for minimizing errors in the decoding process. ###
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