1.5T MRI弥散张量成像技术在脑部恶性肿瘤中的应用价值

目的:探究1. 5T核磁共振成像(MRI)弥散张量成像技术(DTI)在脑部恶性肿瘤中的应用价值。方法:选取2014年9月～2017年8月我院82例脑部肿瘤患者作为研究对象,均进行常规MRI、DTI扫描。对比脑部良恶性肿瘤患者不同区域区扩散系数(ADC值)、各向异性分数(FA值)、相对FA值(r FA值)及常规MRI、DTI检查结果,并分析DTI在脑部恶性肿瘤中的应用价值。结果:术后病理结果显示,82例脑部肿瘤患者中,55例恶性,包括35例胶质瘤,20例转移瘤; 27例良性,包括14例脑膜瘤、6例垂体腺瘤、4例听神经瘤、3例血管母细胞瘤;脑部恶性肿瘤肿瘤实质区、瘤周水肿区及水肿旁蛋白区ADC值、r FA值及FA值均较脑部良性肿瘤低,且同一肿瘤患者不同区域ADC值、r FA值及FA值存在明显差异(P 0. 05); DTI在脑部恶性肿瘤诊断中,敏感性、特异度及准确率均较常规MRI高(P 0. 05)。结论:1. 5 T MRI-DTI用于脑部恶性肿瘤诊断中,可有效区分肿瘤具体部位,进一步提高诊断效果,为治疗方案制定提供有力依据,值得临床推广应用。     

计算机辅助诊断技术中图像纹理研究的主要方法及其应用

目的:综合分析目前图像纹理研究的主要方法以及在医学图像分析中的应用.资料来源:英文文献的检索时间为1983/2009;中文文献的检索时间为2004/2009.以"medical image,texture research,image analysis,application" 检索英文文献;"医学图像,纹理研究,图像分析,应用"检索中文文献.检索数据库包括PubMed数据库(http://www.ncbi.nlm.nih.gov/sites/entrezl),ScienceDirecr数据库(http://www.sciencedirect.comn及中国知网数据库(http://www.cnki.net/. 资料选择:纳入具有原创性的研究论文,及观点明确、资料充分、结论可靠的综述文章,排除重复性研究及与课题相关性较弱的文献.结局评价指标:初检得到104篇文献,包括中文10篇,英文94篇.阅读标题和摘要进行初筛,排除研究目的与课题相关性较弱的33篇,重复性研究18篇,保留53篇中英文文献进一步分析.结果:常用的纹理分析方法包括结构法、统计法、基于模型和基于变换的方法.结构法从纹理的基元形态及其分布规则视角分析规则纹理;统计法主要针对平滑度和粗糙度的纹理特征分析;基于模型的方法以构建图像模型为基础,它不仅可被用于描述纹理,而且还能用于合成纹理;基于变换的方法利用变换域信号处理方法分析纹理的数字特征.纹理研究在医学图像分析中发挥着重要作用,受到广泛关注.结论:由于医学图像及其纹理的特殊性和复杂性,不是所有的纹理测度都能用于医学图像分析.医学图像纹理研究的发展方向之一是如何整合和发挥不同方法的优势,充分提取纹理特征,更准确地表征医学图像纹理及其改变与病理状态的关系,使之成为计算机辅助诊断算法的重要组成部分.     

扩散张量成像在颅内占位性病变中的应用研究

目的:评价MRI扩散张量成像在颅内占位性病变中的应用价值。材料与方法:经手术及病理证实的星形细胞瘤、脑膜瘤、垂体瘤、转移瘤及脑脓肿共46例。行常规MRI扫描、扩散张量成像(DTI)检查。构建各向异性分数(FA)图,并测量病变及周围白质的FA值。结果:正常白质纤维在FA图上表现为高信号。肿瘤多呈低信号。在肿瘤存在时,周围白质纤维可表现为受推压移位或浸润破坏,破坏后FA值降低,表现为低信号。脓肿多呈低信号,其内部若有尚未破坏的白质纤维存在,可呈高信号。这些表现在常规MRI图像上均未清楚显示。结论:DTI可清楚显示占位性病变与周围白质纤维的解剖关系,指导临床制定手术方案。     

多参数MRI影像组学在术前预测胰腺导管腺癌淋巴结转移中的价值

目的 联合常规MRI及表观扩散系数(apparent diffusion coefficient,ADC)图的影像组学特征构建多参数MRI影像组学模型术前预测胰腺导管腺癌(pancreatic ductal adenocarcinoma,PDAC)淋巴结转移(lymph node metastasis,LNM),并与建立的常规MRI影像组学模型和临床模型比较预测效能,探索基于ADC图影像组学的附加价值。材料与方法 218例PDAC按照7∶3的比例随机分为训练集和验证集。纳入临床及常规影像特征构建临床影像学模型。提取常规MRI图像(T1WI、T2WI、动脉期图像及门静脉期图像)及ADC图的影像组学特征。在训练集中采用最小绝对收缩和选择算子筛选出与LNM最相关的特征用于模型构建。构建基于常规MRI影像组学模型(影像组学模型1)和联合常规MRI和ADC图的影像组学模型(影像组学模型2)。使用受试者工作特征曲线下面积(area under the curve,AUC)评估模型预测效能。采用DeLong检验比较模型间的AUC值的差异是否有统计学意义。校准曲线评估模型的准确性。决策曲线分析评估模型...     

Gibbs场先验参数估计在医学图像分割中的应用

背景:基于马尔科夫随机场的图像分割算法已经成为医学图像分割的重要方法,其中,Gibbs场先验参数的取值对分割精度有很大的影响.目的:根据脑部MR图像的成像特点,探讨Gibbs场先验参数的估计方法,从而提高图像分割的精度.方法:通过对脑部MR图像的统计分析,得到图像高斯噪声的方差与Gibbs场先验参数的对应关系.然后在基于马尔可夫随机场图像分割算法的迭代过程中,根据高斯分布的方差估计值,用插值方法估计Gibbs场先验参数.结果与结论:通过对模拟脑部MR图像和临床脑部MR图像分割实验,表明该方法比传统的设定Gibbs场先验参数为某一常数的方法有更精确的图像分割能力,并且实现了图像的自适应分割,具有方法简单、运算速度快、稳健性好的特点.     

肾上腺肿瘤的CT、MRI诊断

目的探讨和比较CT、MRI对肾上腺肿瘤的诊断及鉴别诊断价值.方法回顾性分析204例经手术病理及临床证实的肾上腺肿瘤的CT、MRI表现.结果 CT、MRI对肾上腺肿瘤定位、定性(区分良、恶性肿瘤)及判断肿瘤组织学类型的准确性分别为94.02%、91.85%、89.13%和98.15%、95.15%、90.74%.CT像素矩阵图上,25例(75.76%)肾上腺腺瘤内有轻度负CT值区域,非腺瘤无此征象.13例腺瘤在MRI反相位(OP)图像上的信号强度(SI)都有明显下降,SI指数平均(52.65±18.01)%;非腺瘤的信号强度无明显下降.结论结合临床表现和生化检查,CT、MRI能诊断大部分肾上腺肿瘤,两者的诊断价值相似,MRI对较大肿瘤的定位、定性及判断肿瘤组织学类型有优越性.     

改进模糊C-均值分割算法在多发性硬化症MR脑部图像中的应用

背景:脑部MR图像是一种无纹理的图像,未被噪声污染的脑部MR图像的灰度值具有分片为常数的特点.因此,在聚类过程中灰度值有趋向于在同一个分割区域中相对接近的性质.目的:寻找一个能够自动分割多发性硬化症病灶的模糊C-均值改进方法,为临床对于多发性硬化症的判断提供更方便的工具.方法:考虑到脑部MR图像相邻象素属于同一分类的概率相近的特性,在迭代过程中对8邻域数据集进行滤波以降低噪声对聚类精度的影响,提出了一种改进的模糊C-均值聚类算法.就是将模糊C-均值聚类算法迭代过程中得到的灰度值看作一个数据集,用每个象素邻域象素的灰度值修正该象素的模糊隶属度取值,从而达到利用空间信息抑制噪声的目的.结果与结论:选取了10个多发性硬化症患者的脑部MRI图像进行试验.通过对多发性硬化症患者MR T1脑部图像和T2液体衰减反转回复脑部图像的分割实验,结果显示该算法能够有效分割多发性硬化症病灶,与其他方法所做的多发性硬化症病灶分割相比,本算法更易于实现,运算时间短,同时结果与临床医生的勾画比较重叠率较高,对其临床辅助诊断具有重要作用.     

基于局部特征的医学图像融合方法

目的 介绍一种基于局部小波系数特征的多尺度医学图像融合方法.方法 首先对待融合的两幅医学图像做多尺度的小波分解,然后采用原始图像灰度的局部标准差作为小波系数选取的参考标准,最后再对选取的小波系数进行重构得到最终的融合图像.结果 成功将一幅MRI解剖图像和一幅SPECT功能图像融合在一起.结论 基于局部特征的医学图像融合方法是切实可行的,且简便灵活,图像融合效果较好.     

建立荷人结肠癌裸鼠移植瘤模型及MRI成像检查

背景:肿瘤移植瘤动物模型是探索和模拟肿瘤在人体内生物学行为的一种较为理想的方法,但应用MRI对模型进行评价的研究较少.目的:应用人结肠癌LoVo细胞接种裸鼠,鼠间移植传代,建立人结肠癌裸鼠移植瘤模型,并应用MRI进行初步评价.方法:采用细胞移植和瘤块移植两种方法建立裸鼠移植瘤模型,观察移植瘤大体形态和组织病理学改变,测定宿主血液中的癌胚抗原值,应用免疫组化法观察肿瘤细胞中癌胚抗原的的分布,对6只裸鼠移植瘤模型进行MRI成像检查,并测量肿瘤、肝脏、肌肉组织的T1WI和T2WI的信噪比和三者的T1,T2值.结果与结论:移植瘤的形态和功能特性与原发肿瘤基本相似,移植瘤的移植成功率为100%;6只裸鼠模型的MRI测量结果显示,其肿瘤、肝脏、肌肉的T1WI平均信噪比分别为26.19,22_71,26.621 T2WI平均信噪比分别为9.42,7.66,8.59;平均T1值分别为1 039.22,907.63,1611.51 ms;平均T2值分别为1 09.95,37.31,64.35 ms.结果证实荷人结肠癌裸鼠模型的MRI成像图像清楚,组织分辨率高,测定组织的T1值和T2值可作为定量指标进行分析.     

基于数学形态学人脑MR图像感兴趣区域的提取

背景:在人脑MRI图像中感兴趣区域提取中,应用数学形态学方法取得了较好的效果,但是在抗噪性能和结构元素选取时存在一些不足之处,使得提取效果有缺陷.目的:在数学形态学的基础上,采用一系列改进的数学形态学方法,以期清晰完整地提取人脑MR图像中的感兴趣区域如脑脊液部位,为医学诊断提供准确信息.方法:首先采用复合形态学滤波去除脉冲和高斯噪声,用高低帽变换进行图像增强,然后用形态分水岭阈值分割提取脑部各成分,对分割出的脑脊液图像进行形态开闭滤波、边缘跟踪和灰度填充后,运用抗噪型边缘检测算子检测出清晰完整的脑脊液区域边缘,最后在原图像中用彩色标定,突出感兴趣区域.结果与结论:综合应用多种数学形态学算法,清晰完整地提取了人脑MRI图像中的感兴趣区域--脑脊液部位.经验证,该方法具有简单、快速、精度高、适用性强等特点.     

基于图像分割的Tamura纹理特征算法的研究与实现

背景：ITK主要提供医学图像处理、分割与配准算法,但其缺少可视化的功能,缺乏灵活实用的用户界面,VTK提供了丰富的医学影像处理与分析工具,具有强大的图形处理和可视化功能。目的：利用以前的确诊病例和医生的诊断经验以及患者的相关病史,对确诊的医学影像资源进行管理,归档,并检索,以减少人工干预,提高图像的查全率和查准率。方法：以视觉感知机制为基础,在ITK平台上进行图像平滑去噪和分割的预处理过程,利用Tamura算法完成纹理特征提取,最后通过实验采集、计算数据,完成对比分析。结果与结论：基于图像分割的Tamura纹理特征算法在基于图像纹理检索应用上便于相似性度量,进而可提高检索的准确率。     

Wavelet optimization for content-based image retrieval in medical databases

We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system.     

A deep community based approach for large scale content based X-ray image retrieval

A computer assisted system for automatic retrieval of medical images with similar image contents can serve as an efficient management tool for handling and mining large scale data, and can also be used as a tool in clinical decision support systems. In this paper, we propose a deep community based automated medical image retrieval framework for extracting similar images from a large scale X-ray database. The framework integrates a deep learning-based image feature generation approach and a network community detection technique to extract similar images. When compared with the state-of-the-art medical image retrieval techniques, the proposed approach demonstrated improved performance. We evaluated the performance of the proposed method on two large scale chest X-ray datasets, where given a query image, the proposed approach was able to extract images with similar disease labels with a precision of 85%. To the best of our knowledge, this is the first deep community based image retrieval application on large scale chest X-ray database.     

A graph-based approach for the retrieval of multi-modality medical images

In this paper, we address the retrieval of multi-modality medical volumes, which consist of two different imaging modalities, acquired sequentially, from the same scanner. One such example, positron emission tomography and computed tomography (PET-CT), provides physicians with complementary functional and anatomical features as well as spatial relationships and has led to improved cancer diagnosis, localisation, and staging.The challenge of multi-modality volume retrieval for cancer patients lies in representing the complementary geometric and topologic attributes between tumours and organs. These attributes and relationships, which are used for tumour staging and classification, can be formulated as a graph. It has been demonstrated that graph-based methods have high accuracy for retrieval by spatial similarity. However, naïvely representing all relationships on a complete graph obscures the structure of the tumour-anatomy relationships.We propose a new graph structure derived from complete graphs that structurally constrains the edges connected to tumour vertices based upon the spatial proximity of tumours and organs. This enables retrieval on the basis of tumour localisation. We also present a similarity matching algorithm that accounts for different feature sets for graph elements from different imaging modalities. Our method emphasises the relationships between a tumour and related organs, while still modelling patient-specific anatomical variations. Constraining tumours to related anatomical structures improves the discrimination potential of graphs, making it easier to retrieve similar images based on tumour location.We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes. Our results showed that our method enabled the retrieval of multi-modality images using spatial features. Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques: gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform (SIFT) and relational matrices representing the spatial arrangements of objects.     

影像组学在脑胶质瘤分级中的应用研究

目的利用影像组学与常规磁共振图像对脑胶质瘤进行分级。材料与方法利用BRATS2017公开数据集,自动从图像中的感兴趣区域提取多种特征,包括形态特征、灰度特征、纹理特征等,并运用特征选择方法进行特征选择,最后根据选出的特征对脑胶质瘤的高、低评级进行区分。对支持向量机、决策树与K近邻法等3种分类方法进行比较,也比较了基于遗传算法的包装方法和过滤方法这两种特征选择算法。结果采用过滤方法进行特征选择,支持向量机方法具有最高的准确率91.93%,受试者工作特征曲线下面积(area under the receiver operating characteristic curve,AUC)值为0.90。进一步采用遗传算法进行特征选择,准确率提升至93.33%,AUC值为0.94。结论基于常规磁共振图像,利用影像组学方法,选择合适的特征组合,可以对脑胶质瘤进行自动分级。     

The comparative effectiveness of conventional and digital image libraries

Before introducing a hospital-wide image database to improve access, navigation and retrieval speed, a comparative study between a conventional slide library and a matching image database was undertaken to assess its relative benefits. Paired time trials and personal questionnaires revealed faster retrieval rates, higher image quality, and easier viewing for the pilot digital image database. Analysis of confidentiality, copyright and data protection exposed similar issues for both systems, thus concluding that the digital image database is a more effective library system. The authors suggest that in the future, medical images will be stored on large, professionally administered, centrally located file servers, allowing specialist image libraries to be tailored locally for individual users. The further integration of the database with web technology will enable cheap and efficient remote access for a wide range of users.     

Optimal embedding for shape indexing in medical image databases

This paper addresses the problem of indexing shapes in medical image databases. Shapes of organs are often indicative of disease, making shape similarity queries important in medical image databases. Mathematically, shapes with landmarks belong to shape spaces which are curved manifolds with a well defined metric. The challenge in shape indexing is to index data in such curved spaces. One natural indexing scheme is to use metric trees, but metric trees are prone to inefficiency. This paper proposes a more efficient alternative.We show that it is possible to optimally embed finite sets of shapes in shape space into a Euclidean space. After embedding, classical coordinate-based trees can be used for efficient shape retrieval. The embedding proposed in the paper is optimal in the sense that it least distorts the partial Procrustes shape distance.The proposed indexing technique is used to retrieve images by vertebral shape from the NHANES II database of cervical and lumbar spine X-ray images maintained at the National Library of Medicine. Vertebral shape strongly correlates with the presence of osteophytes, and shape similarity retrieval is proposed as a tool for retrieval by osteophyte presence and severity.Experimental results included in the paper evaluate (1) the usefulness of shape similarity as a proxy for osteophytes, (2) the computational and disk access efficiency of the new indexing scheme, (3) the relative performance of indexing with embedding to the performance of indexing without embedding, and (4) the computational cost of indexing using the proposed embedding versus the cost of an alternate embedding. The experimental results clearly show the relevance of shape indexing and the advantage of using the proposed embedding.     

Case-based similar image retrieval for weakly annotated large histopathological images of malignant lymphoma using deep metric learning

In the present study, we propose a novel case-based similar image retrieval (SIR) method for hematoxylin and eosin (H&E) stained histopathological images of malignant lymphoma. When a whole slide image (WSI) is used as an input query, it is desirable to be able to retrieve similar cases by focusing on image patches in pathologically important regions such as tumor cells. To address this problem, we employ attention-based multiple instance learning, which enables us to focus on tumor-specific regions when the similarity between cases is computed. Moreover, we employ contrastive distance metric learning to incorporate immunohistochemical (IHC) staining patterns as useful supervised information for defining appropriate similarity between heterogeneous malignant lymphoma cases. In the experiment with 249 malignant lymphoma patients, we confirmed that the proposed method exhibited higher evaluation measures than the baseline case-based SIR methods. Furthermore, the subjective evaluation by pathologists revealed that our similarity measure using IHC staining patterns is appropriate for representing the similarity of H&E stained tissue images for malignant lymphoma.     

Semi-supervised cluster analysis of imaging data

In this paper, we present a semi-supervised clustering-based framework for discovering coherent subpopulations in heterogeneous image sets. Our approach involves limited supervision in the form of labeled instances from two distributions that reflect a rough guess about subspace of features that are relevant for cluster analysis. By assuming that images are defined in a common space via registration to a common template, we propose a segmentation-based method for detecting locations that signify local regional differences in the two labeled sets. A PCA model of local image appearance is then estimated at each location of interest, and ranked with respect to its relevance for clustering. We develop an incremental k-means-like algorithm that discovers novel meaningful categories in a test image set. The application of our approach in this paper is in analysis of populations of healthy older adults. We validate our approach on a synthetic dataset, as well as on a dataset of brain images of older adults. We assess our method's performance on the problem of discovering clusters of MR images of human brain, and present a cluster-based measure of pathology that reflects the deviation of a subject's MR image from normal (i.e. cognitively stable) state. We analyze the clusters' structure, and show that clustering results obtained using our approach correlate well with clinical data.