计算机图象图形技术在口腔医学领域中的应用

口腔医学领域中通过形态学的分析作出诊断，制订治疗方案和评价疗效，电子计算机图象形技术对此提供了极大的方便，目前主要的应用领域包括：口腔颌面摄影图象的处理和放射影象计算机技术（如计算机X线断层，磁共振，无胶片X线视频图象系统等）。口腔医学领域中还以计算机处理和表现一些利用特殊方法提取的颅颌面形态图象（如激光全息图）和生理功能图象（如运动轨迹图和咬合接触点分布图等）。近年来，计算机图象图形技术还被广泛地应用于口腔修复体CAD／CAM和口腔医学CAI中，虚拟实现技术也已在口腔医学领域中得到了开展。     

医用眼底图象分析系统的研制

本文研制的眼底图象分析系统可以快速保存患者的眼底图象，同时又可以减少医生仅凭对患者眼底的观察诊断可能带来的主观误差。本文利用ＣＣＤ摄像机把眼底图象取出并通过图象板把眼底图象转化为图象数据，送入计算机进行处理、分析。本系统利用扩充内存（ＥＭＳ）技术完成了同时对３０幅图象（每幅２５６ＫＢ数据）共８ＭＢ数据的实时存储，可以较好地满足医学研究的需要。另外，为了数据保存和传输的需要，根据眼底图象的特征，采用行程编码技术和二维编码技术对图象进行压缩，效果很好     

数学CAD及其在信号分析中的应用

数学ＣＡＤ及其在信号分析中的应用北京科技大学李恪一、数学ＣＡＤ简介随着计算机科学及软件技术的发展，近年来数学ＣＡＤ已逐步发展并日臻完善．数学ＣＡＤ即计算机辅助数学设计，是应用数学和计算机科学的共同产物，它利用用户提供的数学解析符号运算和处理用户输人的...     

上海大学工学院计算机系

上海大学工学院计算机系上海大学工学院成立于１９８３年，专业方向为计算机及应用专业。１９８７年５月．由原系属的ＣＡＤ室晋格为ＣＡＤ研究所，下设图形图象、可靠性、光学／机械ＣＡＤ等三个研究室。系设计算机专业、计算机应用、ＣＡＤ／ＣＡＭ、计算机网络等四个教...     

BP神经网络在奶牛体强判别中的应用研究

根据专家知识从奶牛图象中提取了描述奶牛体强的１２个特征参数；设计了一个三层前向神经网络，选择了一组奶牛图象训练样本。网络训练试验表明，本网络收敛性好且学习次数少；对现场采集的１０头奶牛图象进行奶牛体强判别，判别结果和人工评定的结果基本一致；对全部输人特征参数进行了主变量分析，给出了采用不同数量特征参数的优化组合方案。作者认为神经网络可以取代人工评定奶牛体强。     

从工程图纸到三维CAD模型的研究与进展

以工程图纸的矢量化和实际的ＣＡＤ模型为出发点，本文对和近年来主要集中在矢量化方法的工程图纸计算机处理技术、进展与存在的问题做了回顾。根据工程图纸的属性，和ＣＡＤ技术的高层次需求，介绍了对扫描图形基于图象分析、语法分析和语义分析进而实现三维ＣＡＤ模型重构的思想方法。     

织物纹理的计算机生成技术

纹理是真实感图象最重要的特征之一，而织物纹理是决定服装风格特征的一个重要因素。由于织物纹理的多样性和复杂性，在计算机图形学和服装ＣＡＤ技术的研究中，如何用计算机生成漂亮、逼真的服装面料以及织物纹理就成为一个非常有趣并富有挑战性的问题。本基于Ａｒｎｏｌｄ变换和生命游戏算法提出了一种在计算机上生成多种复杂织物纹理的新方法。实验表明，利用该方法生成的织物纹理既十分漂亮又比较逼真。     

计算机辅助教学──跨世纪的飞跃

计算机辅助教学──跨世纪的飞跃西安交通大学ＣＡＩ中心副主任刘甘娜作为现代教育技术，计算机基础教育（ＣＢＥ）的研究已历经近四十载，而计算机辅助教学（ＣＡＩ）的研究写应用取得了长足的发展。ＣＡＩ研究的壬要内容可分为墓础理论研究、开发技术研究和教学应用研究...     

四川省计算机学会CAD与图形学专业委员会关于召开“96CAD图形图象技术交流与展示会”的通知

四川省计算机学会ＣＡＤ与图形学专业委员会关于召开“９６ＣＡＤ图形图象技术交流与展示会”的通知四川省计算机学会ＣＡＤ与图形学专委会和成都希望电脑公司拟于１９９６年８月中下旬在成都召开“９６ＣＡＤ／图形图象技术交流与展示会”。会议录用的论文将会同《计算机...     

结构复杂数据的半监督聚类

基于成对限制,提出一种半监督聚类算法（ＳＣＣＤ）,它能够处理存在多种密度结构复杂的数据且识别任意形状的簇．利用成对限制反映的多密度分布信息计算基于密度的聚类算法（ＤＢＳＣＡＮ）的邻域半径参数Ｅｐｓ,并利用不同参数的ＤＢＳＣＡＮ 算法处理复杂形状且密度变化的数据集．实验结果表明,ＳＣＣＤ 算法能在噪声环境下发现任意形状且多密度的簇,性能优于已有同类算法．     

Hierarchical contour matching for dental X-ray radiographs

The goal of forensic dentistry is to identify individuals based on their dental characteristics. In this paper we present a new algorithm for human identification from dental X-ray images. The algorithm is based on matching teeth contours using hierarchical chamfer distance. The algorithm applies a hierarchical contour matching algorithm using multi-resolution representation of the teeth. Given a dental record, usually a postmortem (PM) radiograph, first, the radiograph is segmented and a multi-resolution representation is created for each PM tooth. Each tooth is matched with the archived antemortem (AM) teeth, which have the same tooth number, in the database using the hierarchical algorithm starting from the lowest resolution level. At each resolution level, the AM teeth are arranged in an ascending order according to a matching distance and 50% of the AM teeth with the largest distances are discarded and the remaining AM teeth are marked as possible candidates and the matching process proceeds to the following (higher) resolution level. After matching all the teeth in the PM image, voting is used to obtain a list of best matches for the PM query image based upon the matching results of the individual teeth. Analysis of the time complexity of the proposed algorithm prove that the hierarchical matching significantly reduces the search space and consequently the retrieval time is reduced. The experimental results on a database of 187 AM images show that the algorithm is robust for identifying individuals based on their dental radiographs.     

A content-based system for human identification based on bitewing dental X-ray images

This paper presents a system for assisting in human identification using dental radiographs. The goal of the system is to archive antemortem (AM) dental images and enable content-based retrieval of AM images that have similar teeth shapes to a given postmortem (PM) dental image. During archiving, the system classifies the dental images to bitewing, periapical, and panoramic views. It then segments the teeth and the bones in the bitewing images, separates each tooth into the crown and the root, and stores the contours of the teeth in the database. During retrieval, the proposed system retrieves from the AM database the images with the most similar teeth to the PM image based on Hausdorff distance measure between the teeth contours. Experiments on a small database show that our method is effective for dental image classification and teeth segmentation, provides good results for separating each tooth into crown and root, and provides a good tool for human identification.     

三维数字牙颌模型的研制

设计并实现了三维数字牙颌模型的制作;采用的是非接触式三维激光测量技术,基于三角法测量原理来采集数据,采用双CCD接收反射光(也称为双三角测量法),两CCD在激光器左右对称放置,激光器发出的光线经会聚会聚透镜聚焦后垂直入射被测物体表面上,物体移动使成像面上的光点发生位移;数据参数:扫描宽度50 mm,测量景深150 mm,测量精度0.005 mm,控制方式为计算机控制三轴平移,一轴旋转,扫描步距0.01 mm;牙颌石膏模型经三维激光扫描得到三维数字牙颌模型,并经过计算机处理后显示出来;三维数字牙颌模型的研制成功,使口腔医学开始进入牙、颌、面形态的三维精确定量测量与分析阶段.     

A system for human identification from X-ray dental radiographs

Forensic odontology is the branch of forensics that deals with human identification based on dental features. In this paper, we present a system for automating that process by identifying people from dental X-ray images. Given a dental image of a postmortem (PM), the proposed system retrieves the best matches from an antemortem (AM) database. The system automatically segments dental X-ray images into individual teeth and extracts the contour of each tooth. Features are extracted from each tooth and are used for retrieval. We developed a new method for teeth separation based on integral projection. We also developed a new method for representing and matching teeth contours using signature vectors obtained at salient points on the contours of the teeth. During retrieval, the AM radiographs that have signatures closer to the PM are found and presented to the user. Matching scores are generated based on the distance between the signature vectors of AM and PM teeth. Experimental results on a small database of dental radiographs are encouraging.     

An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information

We propose a dental classification and numbering system to effectively segment, classify, and number teeth in dental bitewing radiographs. An image enhancement method that combines homomorphic filtering, homogeneity-based contrast stretching, and adaptive morphological transformation is proposed to improve both contrast and illumination evenness of the radiographs simultaneously. Iterative thresholding and integral projection are adapted to isolate teeth to regions of interest (ROIs) followed by contour extraction of the tooth and the pulp (if available) from each ROI. A binary linear support vector machine using the skew-adjusted relative length/width ratios of both teeth and pulps, and crown size as features is proposed to classify each tooth to molar or premolar. Finally, a numbering scheme that combines a missing teeth detection algorithm and a simplified version of sequence alignment commonly used in bioinformatics is presented to assign each tooth a proper number. Experimental results show that our system has accuracy rates of 95.1% and 98.0% for classification and numbering, respectively, in terms of number of teeth tested, and correctly classifies and numbers the teeth in four images that were reported either misclassified or erroneously numbered, respectively.     

Dental biometrics: alignment and matching of dental radiographs

Dental biometrics utilizes dental radiographs for human identification. The dental radiographs provide information about teeth, including tooth contours, relative positions of neighboring teeth, and shapes of the dental work (e.g., crowns, fillings, and bridges). The proposed system has two main stages: feature extraction and matching. The feature extraction stage uses anisotropic diffusion to enhance the images and a mixture of Gaussians model to segment the dental work. The matching stage has three sequential steps: tooth-level matching, computation of image distances, and subject identification. In the tooth-level matching step, tooth contours are matched using a shape registration method and the dental work is matched on overlapping areas. The distance between the tooth contours and the distance between the dental works are then combined using posterior probabilities. In the second step, the tooth correspondences between the given query (postmortem) radiograph and the database (antemortem) radiograph are established. A distance based on the corresponding teeth is then used to measure the similarity between the two radiographs. Finally, all the distances between the given postmortem radiographs and the antemortem radiographs that provide candidate identities are combined to establish the identity of the subject associated with the postmortem radiographs.     

Individual tooth segmentation from CT images using level set method with shape and intensity prior

3D visualization of teeth from CT images provides important assistance for dentists performing orthodontic surgery and treatment. However, dental CT images present several major challenges for the segmentation of tooth, which touches with adjacent teeth as well as surrounding periodontium and jaw bones. Moreover, tooth contour suffers from topological changes and splits into several branches. In this work, we focus on the segmentation of individual teeth with complete crown and root parts. To this end, we propose adaptive active contour tracking algorithms: single level set method tracking for root segmentation to handle the complex image conditions as well as the root branching problem, and coupled level set method tracking for crown segmentation in order to separate the touching teeth and create the virtual common boundaries between them. Furthermore, we improve the variational level set method in several aspects: gradient direction is introduced into the level set framework to prevent catching the surrounding object boundaries; in addition to the shape prior, intensity prior is introduced to provide adaptive shrinking or expanding forces in order to deal with the topological changes. The test results for both tooth segmentation and 3D reconstruction show that the proposed method can visualize individual teeth with high accuracy and efficiency.