基于Snake模型的医学图像分割

基于Snake模型的图像分割技术是近年来图像处理领域的研究热点之一.Snake模型承载上层先验知识并融合了图像的底层特征,针对医学图像的特殊性,能有效地应用于医学图像的分割中.本文对各种基于Snake模型的改进算法和进化模型进行了研究,并重点梳理了最新的研究成果,以利于把握基于Snake模型的医学图像分割方法的脉络和发展方向.     

基于level sets的医学图像分割

医学图像分割是一个非常重要的研究领域.它主要应用于病人诊断、图像引导手术,以及医学数据可视化.解决这个问题的一个常用方法就是利用活动轮廓或"snake"来分割感兴趣的物体.文中给出两种活动轮廓模型,其中一种基于边缘停止函数,而另一种是一个能量最小化算法.两种方法都采用level-sets模型,利用一个Lipschitz函数φ来进行自动拓扑变化.实验表明第一种方法仅仅只能检测边缘梯度较大的物体,而第二种方法没有这样的限制.     

一种基于KPCA和形状先验知识的图像分割模型

对含有噪声、遮挡和信息缺失的图像进行分割,如果仅使用图像自身信息难以得到满意的结果。因此,本研究提出了一种新的融合图像信息和形状先验知识的可变形模型。在Chen等人的工作基础上,提出用核主元分析(KPCA)代替主元分析(PCA)来捕获形状信息。KPCA能更好地表示形状先验知识,允许待分割的目标形状与先验形状存在较大差异或非线性变形,而PCA需两者足够接近。同时,所用的分割模型包含了图像信息项和形状先验项,充分考虑了在分割过程中平衡全局图像信息和形状先验知识的相互作用。将本研究的模型和基于PCA的分割模型应用于合成图像和医学CT图像,结果表明KPCA更能准确地识别出与先验形状差异较大或背景污染严重的目标物体。     

基于活动轮廓模型的图像分割算法综述

活动轮廓模型是一种重要的图像分割技术,它利用底层信息,并结合高层先验知识,实现对复杂目标轮廓的自动分割。自Kass等提出该思想以来的20多年中,活动轮廓模型在理论研究和应用方面均取得长足发展。首先,介绍活动轮廓模型的发展历程,重点阐述并分析典型的参数活动轮廓模型和几何活动轮廓模型,进而扼要介绍混合活动轮廓模型和快速求解算法;随后,从理论基础、分割效果、算法效率以及应用等方面,比较两类模型之间的区别与联系;最后,对活动轮廓模型未来的发展趋势进行展望。     

一种活动形状模型中的二维灰度模型

活动形状模型(active shape models,ASM)是一种较为流行的用于目标对象定位的统计学形状模型.该模型结合了一个点分布模型(point distribution model,PDM)和每个标志点周围的局部灰度模型,从而能够获取可靠精确的图像解释结果.对于二维图像,一维曲线的灰度模型会丟失其它方向上的信息.本文对于每个标志点周围的正方形邻域的灰度信息建立了模型,希望能够使活动形状模型方法考虑到更多的信息.     

一种脑MRA图像自动分割算法及其在图像导引神经外科中的应用

图像导引神经外科技术是在神经外科手术过程中减小患者刨伤的有效方法。在这种方法中，医学图像分割质量的好坏直接影响着手术过程的准确性。本研究提出了一种新的基于MRA图像的自动分割算法，这种算法通过各向异性滤波，统计阈值分割，数学形态学滤波，和基于边界距离场的活动轮廓模型来对MRA图像进行自动分割和分割结果的可视化。数据实验表明用这种算法对MRA图像进行分割的结果可以有效地用于图像导引神经外科，并且算法具有一定的鲁棒性。     

医学图像实例分割:从有候选区域向无候选区域EI北大核心CSCD

医学图像中目标的检测和分割任务是近年来图像处理领域中的研究热点和难点。实例分割为属于同一类的不同对象提供实例级标签,因此广泛应用于医学图像处理领域。本文对医学图像实例分割从以下几个方面进行总结:第一,阐述实例分割的基本原理,将实例分割模型归纳为三类,并采用二维空间展示实例分割算法发展脉络,给出六个实例分割经典模型图;第二,从两阶段实例分割、单阶段实例分割以及三维(3D)实例分割三类模型的角度出发,分别总结三类模型的思想,探讨优缺点和梳理最新发展;第三,总结了实例分割在结肠组织图像、宫颈图像、骨显像图像、胃癌病理切片图像、肺结节计算机断层扫描图像和乳腺X线片图像等六种医学图像的应用现状;第四,讨论当前医学图像实例分割领域面对的主要挑战,并展望未来的发展方向。本文系统总结实例分割的原理、模型、特点,以及实例分割在医学图像处理领域中的应用,对实例分割的研究具有积极的指导意义。     

DICOM医学图像扩展模型的研究

医学图像存档和通讯系统PACS(Picture Archivingand Communication System)促进了医学图像的共享，但是该系统只能提供基于关键字的医学图像检索，不能进行基于语义内容检索，本研究在现有图像处理、分析和理解技术的基础上对医学图像的内容信息进行了划分，并对PACS系统的医学成像和通讯标准DICOM(Digital Imaging and Communicationin Medicine)信息模型进行了扩充，增加有关图像内容的信息，提出了DICOM信息模型的扩展模型。通过该模型来组织医学信息的存储和检索可以满足基于关键词和基于图像语义内容检索的需要，最后在该模型的框架下实现了超声心动图像基于形状特征的检索。     

基于3D Shepp-Logan头部模型的三维医学图像重建仿真

Shepp-Logan头部模型是计算机断层图像重建(CT)领域仿真计算普遍采用的经典模型。我们提出一种新思路—以3D Shepp-Logan头部模型作为三维医学图像重建领域进行仿真实验和算法性能评测的基本参考模型。首先介绍了3D Shepp-Logan头部模型的设计与实现以及仿真投影数据的计算,进而描述了所设计的三维医学图像重建仿真计算过程。数值实验部分给出了基于3D Shepp-Logan头部模型的三维医学图像重建仿真实验。实验结果表明了新思路的可行性和模型计算的准确性。     

医学图像分割中的主动轮廓模型研究现状

主动轮廓模型具有强大的先验知识引入能力,非常适合解决复杂医学图像分割问题。本文介绍了两种主动轮廓模型的基本原理及其相互关系,详细综述了模型的几个重要改进措施,包括曲线的表示方式、基于梯度的ACM、基于区域的ACM,以及结合先验形状的ACM,并讨论了医学图像分割中的主要应用实例,最后展望了模型今后的研究方向。     

一种基于活动形状模型算法的耳郭穴区定位方法

目的耳穴疗法是中医的重要组成部分,而耳穴定位是治疗的前提。由于耳郭面积较小、耳穴众多导致自动寻穴困难,本文提出一种基于图像处理技术的耳郭穴区定位方法。方法通过对来自于北京科技大学USTB人耳图像库和自行采集图像共30幅构建训练集,在最新耳穴国家标准GB/T13734-2008的基础上,选取包含耳郭边缘点、曲率极大值点、等分点等在内的65个特征点构建点分布模型(point distribution model,PDM),采用基于活动形状模型(active shape model,ASM)的图像处理方法,对待测人耳图像搜索匹配后,通过分别连接构成耳郭穴区的特征点,实现对人耳图像的穴区定位。最后通过计算定位后与人工标定的特征点之间的欧几里得距离,对定位的精度进行评估。结果该方法能初步实现耳郭的穴区定位,欧几里得平均距离为6.246±0.429。结论基于ASM的耳郭穴区定位方法能初步实现人耳图像的穴区定位,有利于耳穴疗法的穴区定位、耳穴自动化仪器的开发和中医耳穴示教等。     

Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans

Femur segmentation can be an important tool in orthopedic surgical planning. However, in order to overcome the need of an experienced user with extensive knowledge on the techniques, segmentation should be fully automatic. In this paper a new fully automatic femur segmentation method for CT images is presented. This method is also able to define automatically the medullary canal and performs well even in low resolution CT scans.Fully automatic femoral segmentation was performed adapting a template mesh of the femoral volume to medical images. In order to achieve this, an adaptation of the active shape model (ASM) technique based on the statistical shape model (SSM) and local appearance model (LAM) of the femur with a novel initialization method was used, to drive the template mesh deformation in order to fit the in-image femoral shape in a time effective approach.With the proposed method a 98% convergence rate was achieved. For high resolution CT images group the average error is less than 1 mm. For the low resolution image group the results are also accurate and the average error is less than 1.5 mm.The proposed segmentation pipeline is accurate, robust and completely user free. The method is robust to patient orientation, image artifacts and poorly defined edges. The results excelled even in CT images with a significant slice thickness, i.e., above 5 mm. Medullary canal segmentation increases the geometric information that can be used in orthopedic surgical planning or in finite element analysis.     

Gallbladder shape extraction from ultrasound images using active contour models

Gallbladder function is routinely assessed using ultrasonographic (USG) examinations. In clinical practice, doctors very often analyse the gallbladder shape when diagnosing selected disorders, e.g. if there are turns or folds of the gallbladder, so extracting its shape from USG images using supporting software can simplify a diagnosis that is often difficult to make. The paper describes two active contour models: the edge-based model and the region-based model making use of a morphological approach, both designed for extracting the gallbladder shape from USG images. The active contour models were applied to USG images without lesions and to those showing specific disease units, namely, anatomical changes like folds and turns of the gallbladder as well as polyps and gallstones. This paper also presents modifications of the edge-based model, such as the method for removing self-crossings and loops or the method of dampening the inflation force which moves nodes if they approach the edge being determined. The user is also able to add a fragment of the approximated edge beyond which neither active contour model will move if this edge is incomplete in the USG image. The modifications of the edge-based model presented here allow more precise results to be obtained when extracting the shape of the gallbladder from USG images than if the morphological model is used.     

基于最小方差Snake模型的医学图像分割

传统的参数活动轮廓模型（Snake模型）难于处理自动分割医学图像的弱边界。我们在分析参数活动轮廓和几何活动轮廓模型的基础上，提出最小方差参数活动轮廓模型，并成功应用于医学图像自动分割。该方法将气球力Snake模型中的恒定气球力修改为包含区域信息的变力，以目标和背景两区域具有最小方差为准则，引导轮廓线演化。实验结果表明，该模型对初始轮廓位置不敏感，能实现自动分割。对于带噪声的医学图像，先进行保边界特性的曲率流滤波，然后应用该模型也能取得满意的分割效果。     

The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration

Background

Statistical shape models are widely used in biomedical research. They are routinely implemented for automatic image segmentation or object identification in medical images. In these fields, however, the acquisition of the large training datasets, required to develop these models, is usually a time-consuming process. Even after this effort, the collections of datasets are often lost or mishandled resulting in replication of work.

Objective

To solve these problems, the Virtual Skeleton Database (VSD) is proposed as a centralized storage system where the data necessary to build statistical shape models can be stored and shared.

Methods

The VSD provides an online repository system tailored to the needs of the medical research community. The processing of the most common image file types, a statistical shape model framework, and an ontology-based search provide the generic tools to store, exchange, and retrieve digital medical datasets. The hosted data are accessible to the community, and collaborative research catalyzes their productivity.

Results

To illustrate the need for an online repository for medical research, three exemplary projects of the VSD are presented: (1) an international collaboration to achieve improvement in cochlear surgery and implant optimization, (2) a population-based analysis of femoral fracture risk between genders, and (3) an online application developed for the evaluation and comparison of the segmentation of brain tumors.

Conclusions

The VSD is a novel system for scientific collaboration for the medical image community with a data-centric concept and semantically driven search option for anatomical structures. The repository has been proven to be a useful tool for collaborative model building, as a resource for biomechanical population studies, or to enhance segmentation algorithms.     

基于先验知识和几何主动轮廓线的三维超声瓣膜分割

几何主动轮廓线模型是一种有效的图像分割方法.但对于被噪声严重干扰的目标依然很难准确分割.特定目标的先验知识可以有效地指导目标的准确分割.我们把特定目标的区域和形状先验知识表示成一种速度场,把此速度场嵌入到几何主动轮廓线模型中,指导目标的快速准确分割.先验区域限制水平集在特定区域迭代,先验形状使曲线向理想轮廓演化.我们把该算法应用于三维超声图像的二尖瓣自动分割,结果表明该分割算法是快速和高效的.     

Differences in airway remodeling between subjects with severe and moderate asthma

BACKGROUND: Airway remodeling in asthma comprises a range of structural changes. Several studies have suggested an association between these changes and disease severity. The relationship between the extent of remodeling and lung function is not well defined. OBJECTIVE: We sought to contrast the structural changes in the airways of well-defined groups of subjects with severe and moderate asthma and to correlate the extent of remodeling with disease severity. METHODS: Endobronchial biopsy specimens were obtained from 15 subjects with severe and 13 subjects with moderate asthma. Epithelial integrity, cell-layer areas, subepithelial fibrosis, and the distance between epithelial and airway smooth muscle (ASM) layers were measured by means of image analysis. Collagen was identified by using Van Giesen stain, and ASM was defined by using smooth muscle alpha-actin immunostaining. Specific immunostains were performed for the evaluation of RANTES, IL-8, and eotaxin expression as markers of ASM phenotype. RESULTS: ASM area was greater in subjects with severe (0.24+/- 0.03 mm(2)) than in subjects with moderate (0.05+/- 0.01 mm(2)) asthma (P<.001). The distance between the epithelial and ASM layers was less in the severe group (0.12+/- 0.01 mm) than in the moderate group (0.24+/- 0.02, P<.001). A trend toward greater subepithelial fibrosis in subjects with severe asthma did not reach statistical significance. IL-8 and eotaxin expression, but not RANTES expression, were increased in the ASM of subjects with severe asthma compared with in subjects with moderate asthma. CONCLUSION: Smooth muscle alteration is the key structural change that distinguishes severe from moderate asthma, and phenotypic change in ASM might contribute to the difficulty in obtaining adequate control in some subjects with severe asthma.