让更多人享受CT扫描的服务

CT成像结合了特殊的X射线设备和先进的电脑来生成人体内部的二维和三维图像。内部器官、骨骼、软组织、软组织和血管的CT扫描可提供比X射线检查更具体的图像，使医生能够更容易地诊断疾病，包括癌症、心血管疾病以及肌肉骨骼疾患。随着技术的不断进步，新型的CT（计算机断层扫描）扫描设备已可提供更清晰、更具体的人体图片以供医生分析和诊断。     

让更多人享受CT扫描的服务ADI新款数据采集芯片使得CT扫描仪能够更快速地生成更清晰的图像

CT成像结合了特殊的X射线设备和先进的电脑来生成人体内部的二维和三维图像.内部器官、骨骼、软组织、软组织和血管的CT扫描可提供比X射线检查更具体的图像,使医生能够更容易地诊断疾病,包括癌症、心血管疾病以及肌肉骨骼疾患.随着技术的不断进步,新型的CT(计算机断层扫描)扫描设备已可提供更清晰、更具体的人体图片以供医生分析和诊断.     

智慧人体

医生开始3D打印制造器官和骨骼 目前，日本医生可以使用患者的CT和MRI图像，利用3D打印创建真人大小的复制器官。它们具有真实器官的坚定性和纹理，并且为半透明，可从外部直接看到结构和骨骼。当然，专业的3D打印机价格相当不菲，通常要花费25万美元到50万美元。     

基于回归的X线胸片肺野自动分割算法

X线胸片中肺野的精确定位与区分是X线胸片辅助诊断系统的必要步骤，它在准确定位不同肺野位置后可以与病灶检测模块相结合并输出自动化报告，从而辅助医生进行诊断。基于此，提出一种基于回归的肺野分割算法，能够快速准确地完成肺野分割。在临床试验中，对比多种分割及分类算法，该算法具有更高的准确率和灵敏度。     

一种基于多小波变换的医学图像融合方法

本文讨论了医学图像CT与NMR的融合问题。由于骨骼组织仅能在CT中清晰显示,而软组织仅能在NMR中清晰显示,所以CT与NMR图像各自都不能同时清晰显示骨骼组织和软组织。在分析了CT与NMR图像成像机理的基础上,提出了一种基于多小波变换的融合方法。它可将CT与NMR图像进行有效的综合。所获得的融合图像,可同时清晰地显示骨组织和软组织信息。     

基于Python的肺结核胸片病灶检测系统设计

肺结核病是危害人类健康的重大传染病之一,如何快速高效地找出病灶是影像医生工作的一大难点。本文设计了基于Python的肺结核胸片病灶检测系统。该系统设计了用户的注册登录模块、胸片识别模块、信息管理模块,系统核心是Efficientdet算法对胸片进行图像识别标出病灶区域。本系统可用于小型医院肺结核胸片检测,标出病灶区域,使医生更快地对胸片作出诊断,找出病灶减少差错,提高肺结核的诊断效率,减少影像医生的工作量使其提高工作效率。     

Maya骨骼动画入门

本文将学习Maya骨骼动画的基础知识。创建关节链从建立骨骼开始,关节链由一系列的关节和它们所连接的骨头组成,然后可添加关节链,可通过延伸关节链的方式添加,也可从任何关节链的关节开始添加新的关节链。用这种方式可创建多样的关节链和肢体链的复杂结构,这些关节链和肢体链定义了骨骼的运动层次。骨骼是有层次的关节结构,它可使已被蒙皮的可变形物体活动和定位,并提供了动画层次动作的结构。关节是骨骼中骨头和骨头之间的连接点,每个关节可连接一个或多块骨头。关节控制着骨头的旋转和移动。关节属性可以设置关节的运动,比如限制关节旋转…     

低对比度胸片中管线检测方法

一般在重症监护病房中常用便携式胸片机来辅助医生监控患者的病情进展,了解各种医用管线在病人体内的具体位置。但便携式X光机得到的胸片有着低对比度、噪声多的缺陷,且胸片中的管线并不清晰,使得医生不便于观测管线的位置。文中提出一种在ICU病房中胸片的管线检测方法,在得到病人的胸片后,使用对比度限制的自适应直方图均衡化处理方法来调整对比度,再对其进行双边滤波来去除噪声,同时增强管线的细节信息,然后再做管线检测。将文中方法应用在100余张便携式胸片中,结果显示所提方法可有效检测医用管线,且有效准确率接近90%。     

成年女性错[牙合]畸形患者颅颌面软硬结构关系的研究

目的：研究成年女性错[牙合]畸形患者软组织侧貌特征与颅颌面骨骼及牙齿的相关性,找出对软组织侧貌影响明显的骨性或牙性畸形。材料与方法：收集120例成年女性错[牙合]畸形患者（年龄18岁-26岁,平均年龄23．5岁）治疗前头颅侧位片,利用Delaire头影测量分析方法测量颅颌面骨骼结构异常程度及鼻唇颏部侧貌软组织突度及相关...     

一种全新的两步自动化医学图像分割方案

针对现有图像分割方法存在需要手动分割,以及精确度较低的问题。采用一种全新的两步图像分割方案。该方案。以基于人工神经网络的模式识别技术,即人工神经网络的大规模培训的方法,通过对肺区不同子区域内结构进行分割处理,利用训练好的大规模人工神经网络对标准胸片中的肋骨、锁骨等骨质结构进行抑制,结合以基于区域的活动轮廓模型,即Snake模型,正确分割亮度不均匀的图像。文中选择与医护人员人工分割的图像进行对比,通过放射科医生采用等级法打分,原图的平均分为20分,而通过文中改进的分割方法平均分高达34分。     

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

When lung nodules overlap with ribs or clavicles in chest radiographs, it can be difficult for radiologists as well as computer-aided diagnostic (CAD) schemes to detect these nodules. In this paper, we developed an image-processing technique for suppressing the contrast of ribs and clavicles in chest radiographs by means of a multiresolution massive training artificial neural network (MTANN). An MTANN is a highly nonlinear filter that can be trained by use of input chest radiographs and the corresponding "teaching" images. We employed "bone" images obtained by use of a dual-energy subtraction technique as the teaching images. For effective suppression of ribs having various spatial frequencies, we developed a multiresolution MTANN consisting of multiresolution decomposition/composition techniques and three MTANNs for three different-resolution images. After training with input chest radiographs and the corresponding dual-energy bone images, the multiresolution MTANN was able to provide "bone-image-like" images which were similar to the teaching bone images. By subtracting the bone-image-like images from the corresponding chest radiographs, we were able to produce "soft-tissue-image-like" images where ribs and clavicles were substantially suppressed. We used a validation test database consisting of 118 chest radiographs with pulmonary nodules and an independent test database consisting of 136 digitized screen-film chest radiographs with 136 solitary pulmonary nodules collected from 14 medical institutions in this study. When our technique was applied to nontraining chest radiographs, ribs and clavicles in the chest radiographs were suppressed substantially, while the visibility of nodules and lung vessels was maintained. Thus, our image-processing technique for rib suppression by means of a multiresolution MTANN would be potentially useful for radiologists as well as for CAD schemes in detection of lung nodules on chest radiographs.     

Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network

Low-dose helical computed tomography (LDCT) is being applied as a modality for lung cancer screening. It may be difficult, however, for radiologists to distinguish malignant from benign nodules in LDCT. Our purpose in this study was to develop a computer-aided diagnostic (CAD) scheme for distinction between benign and malignant nodules in LDCT scans by use of a massive training artificial neural network (MTANN). The MTANN is a trainable, highly nonlinear filter based on an artificial neural network. To distinguish malignant nodules from six different types of benign nodules, we developed multiple MTANNs (multi-MTANN) consisting of six expert MTANNs that are arranged in parallel. Each of the MTANNs was trained by use of input CT images and teaching images containing the estimate of the distribution for the "likelihood of being a malignant nodule," i.e., the teaching image for a malignant nodule contains a two-dimensional Gaussian distribution and that for a benign nodule contains zero. Each MTANN was trained independently with ten typical malignant nodules and ten benign nodules from each of the six types. The outputs of the six MTANNs were combined by use of an integration ANN such that the six types of benign nodules could be distinguished from malignant nodules. After training of the integration ANN, our scheme provided a value related to the "likelihood of malignancy" of a nodule, i.e., a higher value indicates a malignant nodule, and a lower value indicates a benign nodule. Our database consisted of 76 primary lung cancers in 73 patients and 413 benign nodules in 342 patients, which were obtained from a lung cancer screening program on 7847 screenees with LDCT for three years in Nagano, Japan. The performance of our scheme for distinction between benign and malignant nodules was evaluated by use of receiver operating characteristic (ROC) analysis. Our scheme achieved an Az (area under the ROC curve) value of 0.882 in a round-robin test. Our scheme correctly identified 100% (76/76) of malignant nodules as malignant, whereas 48% (200/413) of benign nodules were identified correctly as benign. Therefore, our scheme may be useful in assisting radiologists in the diagnosis of lung nodules in LDCT.     

Patient-specific models for lung nodule detection and surveillancein CT images

The purpose of this work is to develop patient-specific models for automatically detecting lung nodules in computed tomography (CT) images. It is motivated by significant developments in CT scanner technology and the burden that lung cancer screening and surveillance imposes on radiologists. We propose a new method that uses a patient's baseline image data to assist in the segmentation of subsequent images so that changes in size and/or shape of nodules can be measured automatically. The system uses a generic, a priori model to detect candidate nodules on the baseline scan of a previously unseen patient. A user then confirms or rejects nodule candidates to establish baseline results. For analysis of follow-up scans of that particular patient, a patient-specific model is derived from these baseline results. This model describes expected features (location, volume and shape) of previously segmented nodules so that the system can relocalize them automatically on follow-up. On the baseline scans of 17 subjects, a radiologist identified a total of 36 nodules, of which 31 (86%) were detected automatically by the system with an average of 11 false positives (FPs) per case. In follow-up scans 27 of the 31 nodules were still present and, using patient-specific models, 22 (81%) were correctly relocalized by the system. The system automatically detected 16 out of a possible 20 (80%) of new nodules on follow-up scans with ten FPs per case.     

Lung No dules Detection in CT Images Using Gestalt-Based Algorithm

To overcome low accuracy and high false positive of existing computer-aided lung nodules detec-tion. We propose a novel lung nodule detection scheme based on the Gestalt visual cognition theory. The pro-posed scheme involves two parts which simulate human eyes cognition features such as simplicity, integrity and classification. Firstly, lung region was segmented from lung Computed tomography (CT) sequences. Then local three-dimensional information was integrated into the Maximum intensity projection (MIP) images from axial, coronal and sagittal profiles. In this way, lung nodules and vascular are strengthened and discriminated based on pathologic image characteristics of lung nodules. The experimental database includes fifty-three high resolution CT images contained lung nodules, which had been confirmed by biopsy. The experimental results show that, the accuracy rate of the proposed algorithm achieves 91.29%. The proposed frame-work improves performance and computation speed for computer aided nodules detection.     

一种基于CT影像的肺癌病灶检测新方法

 肺癌病灶的检测一直是重要与困难的工作,本文提出了一种基于三维CT影像的肺结节计算机辅助检测新方法.基于自适应阈值等方法分割肺实质区域;由于肺血管是肺结节检测的重要干扰,建立一种形变模型精确分割并过滤肺内血管组织;基于Hessian矩阵特征值构造可选择形状滤波器检测疑似结节,并进一步过滤剩余的细小血管组织;提取多个结节特征,并采用基于规则分类器进行分类.实验结果表明,该方法可以有效帮助医生提高肺癌疾病的诊断准确率.     

一种新策略的肺结节检测算法

针对计算机在肺部CT肺结节辅助检测过程中,二维CT图像序列血管横截面与肺结节成像特征类似,导致无法有效检测的问题,提出新策略的肺结节检测算法。以格式塔心理学为基础,以去除血管的新策略间接的对肺结节进行检测。实验结果表明,本算法可有效降低血管对肺结节检测的影响,从而提高肺结节的检测精度。     

Relations Between Fractional-Order Model Parameters and Lung Pathology in Chronic Obstructive Pulmonary Disease

In this study, changes in respiratory mechanics from healthy and chronic obstructive pulmonary disease (COPD) diagnosed patients are observed from identified fractional-order (FO) model parameters. The noninvasive forced oscillation technique is employed for lung function testing. Parameters on tissue damping and elastance are analyzed with respect to lung pathology and additional indexes developed from the identified model. The observations show that the proposed model may be used to detect changes in respiratory mechanics and offers a clear-cut separation between the healthy and COPD subject groups. Our conclusion is that an FO model is able to capture changes in viscoelasticity of the soft tissue in lungs with disease. Apart from this, nonlinear effects present in the measured signals were observed and analyzed via signal processing techniques and led to supporting evidence in relation to the expected phenomena from lung pathology in healthy and COPD patients.      

Automated segmentation refinement of small lung nodules in CT scans by local shape analysis

One of the most important problems in the segmentation of lung nodules in CT imaging arises from possible attachments occurring between nodules and other lung structures, such as vessels or pleura. In this report, we address the problem of vessels attachments by proposing an automated correction method applied to an initial rough segmentation of the lung nodule. The method is based on a local shape analysis of the initial segmentation making use of 3-D geodesic distance map representations. The correction method has the advantage that it locally refines the nodule segmentation along recognized vessel attachments only, without modifying the nodule boundary elsewhere. The method was tested using a simple initial rough segmentation, obtained by a fixed image thresholding. The validation of the complete segmentation algorithm was carried out on small lung nodules, identified in the ITALUNG screening trial and on small nodules of the lung image database consortium (LIDC) dataset. In fully automated mode, 217/256 (84.8%) lung nodules of ITALUNG and 139/157 (88.5%) individual marks of lung nodules of LIDC were correctly outlined and an excellent reproducibility was also observed. By using an additional interactive mode, based on a controlled manual interaction, 233/256 (91.0%) lung nodules of ITALUNG and 144/157 (91.7%) individual marks of lung nodules of LIDC were overall correctly segmented. The proposed correction method could also be usefully applied to any existent nodule segmentation algorithm for improving the segmentation quality of juxta-vascular nodules.     

基于多模型融合方法的肺结节良恶性分类

针对CT图像中肺结节因边缘模糊、特征不明显造成的分类效果有偏差的问题,本文提出一种嵌入注意力机制的多模型融合方法(简称MSMA-Net).该方法先将原始CT图像进行肺实质分割和裁剪操作后得到两种不同尺寸的图像,然后分别输入到空间注意力模型和通道注意力模型进行训练,其中,空间注意力模型着重于提取肺结节在CT图像中的空间位...     

基于空间分布的三维自动化肺结节分割算法

针对肺结节分割中存在的自动化程度低、较少考虑空间结构以及粘附型肺结节分割不充分问题,提出了一种基于空间分布的三维自动化肺结节分割算法.该算法首先利用C-means聚类算法分割出肺实质,然后根据肺结节空间分布的差异性将其分为3类:孤立性肺结节、胸膜粘附性肺结节、血管粘附性肺结节,并对3种不同类型的肺结节分别采用基于连通性、灰度下降和散度差异的分割算法进行分割,70个肺结节(其中孤立性肺结节38个,血管粘附性肺结节17个,胸膜粘附性肺结节15个)CT图像的实验结果表明,算法能够准确、自动地分割出3种不同部位的肺结节.