论文阅读:Automatic Tooth Region Separation for Dental CT Images

【论文信息】
Automatic Tooth Region Separation for Dental CT Images
Hui Gao 2008 年发表的会议论文
【背景】
提出了该领域研究内容:In order to reconstruct each tooth model from images we need to handle the following problems such as separation of the upper and lower jaws, detection of the orthodontic features, fitting of the dental arch, determination of the tooth interstice, segmentation of the tooth boundary, etc.
用全景图分离牙齿在某些切片可能会出错,毕竟全景图丢失了三维信息。

本文方法还是从积分投影最小的角度出发,不过是在3D数据上面计算。沿着牙弓线每个点都计算积分投影。每个切片都拟合牙弓线,选一条最长的。(现在的方法不需要了)

【方法】
首先拟合牙弓线,然后基于牙弓线积分投影平面分离相邻牙齿。
这里写图片描述

内容概要:本文详细介绍了如何使用STM32微控制器精确控制步进电机,涵盖了从原理到代码实现的全过程。首先,解释了步进电机的工作原理,包括定子、转子的构造及其通过脉冲信号控制转动的方式。接着,介绍了STM32的基本原理及其通过GPIO端口输出控制信号,配合驱动器芯片放大信号以驱动电机运转的方法。文中还详细描述了硬件搭建步骤,包括所需硬件的选择与连接方法。随后提供了基础控制代码示例,演示了如何通过定义控制引脚、编写延时函数和控制电机转动函数来实现步进电机的基本控制。最后,探讨了进阶优化技术,如定时器中断控制、S形或梯形加减速曲线、微步控制及DMA传输等,以提升电机运行的平稳性和精度。 适合人群:具有嵌入式系统基础知识,特别是对STM32和步进电机有一定了解的研发人员和技术爱好者。 使用场景及目标:①学习步进电机与STM32的工作原理及二者结合的具体实现方法;②掌握硬件连接技巧,确保各组件间正确通信;③理解并实践基础控制代码,实现步进电机的基本控制;④通过进阶优化技术的应用,提高电机控制性能,实现更精细和平稳的运动控制。 阅读建议:本文不仅提供了详细的理论讲解,还附带了完整的代码示例,建议读者在学习过程中动手实践,结合实际硬件进行调试,以便更好地理解和掌握步进电机的控制原理和技术细节。同时,对于进阶优化部分,可根据自身需求选择性学习,逐步提升对复杂控制系统的理解。
As an AI language model, I can suggest some possible steps for automatic tooth instance segmentation and identification from cone beam CT images: 1. Pre-processing: The first step is to pre-process the CBCT images to remove noise, artifacts, and other image distortions. This can be achieved through various image processing techniques, such as filtering, thresholding, and morphological operations. 2. Tooth segmentation: The next step is to segment the individual teeth from the CBCT images. This can be done using various segmentation algorithms, such as region-growing, active contours, and watershed-based techniques. The segmentation can be further refined by incorporating shape and texture information. 3. Tooth instance identification: Once the individual teeth are segmented, the next step is to identify the specific tooth instance (e.g., mandibular left central incisor). This can be done using various methods, such as template matching, feature extraction, and machine learning-based approaches. 4. Post-processing: Finally, the segmented teeth and identified instances can be post-processed to remove any remaining artifacts or noise. This can be achieved through various techniques, such as smoothing, filtering, and morphological operations. Overall, automatic tooth instance segmentation and identification from CBCT images is a challenging task that requires the integration of various image processing, computer vision, and machine learning techniques. However, with recent advancements in AI and deep learning, there is great potential for developing accurate and reliable automated systems for dental image analysis.
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