基于期望值最大化方法的磁共振图像人脑组织分割

目的利用期望值最大化方法进行磁共振图像的人脑组织分割。方法在分析当前常用的医学图像分割方法的基础上,提出一种基于统计理论的期望值最大化分割方法,并给出了相应的理论算法模型和实现步骤,最后用Visual C 6.0编程,并对磁共振大脑图像进行实验,并与应用SPM软件对同一幅图像的分割结果进行分析比较。结果本文分割方法与SPM软件的分割结果非常接近,大脑灰质、白质、脑脊液等组织之间边界清晰,总体不确定性较小。结论本文分割方法切实可行,分割效果较好,为进一步的磁共振图像分析和疾病研究提供了一种有效工具。     

颈动脉MR图像分割和三维重建在斑块定位中的应用

背景:医学图像的三维重建在医疗诊断、实验分析中起着越来越重要的作用,它是一项复杂的任务,其中目标图像的分割是首要且重要的一步。目的:探索对颈动脉MR图像的图像分割及三维重建方法,并探讨三维模型在颈动脉斑块定位中的应用。方法:选择3DTOF序列图像对其进行基于最大熵原理的阈值分割,并与普通方法的结果做比较;进一步用数学形态学分割方法提取出颈动脉;进行三维重建,利用三维模型进行斑块的初步定位。结果与结论:基于最大熵原理的阈值分割适于对颈动脉3DTOF序列图像的分割,用数学形态学分割方法进行后续分割可得到目标图像。三维重建后的模型对于斑块定位有辅助作用。     

基于阈值分割和Snake模型的弱边缘医学超声图像自动分割

医学超声图像分割是图像处理中的一项关键技术.文章以胆结石超声图像为例,介绍一种新的弱边缘超声图像自动分割算法.首先采用基于直方图凹度分析的闽值分割方法确定Snake模型的初始蛇,再基于Snake模型结合贪婪算法对图像进行目标分割.实验结果表明该算法对弱边缘现象较为严重的医学超声图像进行目标分割时,定位准确,分割效果良好,足一种全自动的超声医学图像分割方法.     

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.     

肝癌图像感兴趣目标的准确分割

背景:Snake模型为医学图像分割提供了一个全新的分割方式,可以克服传统图像分割方法在医学图像分割中的缺点.目的:针对肝癌CT图像特点,提出了一种改进的B样条曲线的Snake模型图像分割算法.方法:对腹部CT图像进行预处理,获得肝脏癌变部分的初始轮廓,再构造闭合B样条Snake模型,最后使用MMSE最小化外力变形模型以实现图像的准确分割.结果与结论:改进的B-Snake分割算法不仅减少了噪声的影响,而且使Snake曲线较好地收敛于目标轮廓边缘,对于肝癌CT图像该方法取得了感兴趣目标的良好分割效果.     

医学图像背景分割

目的 探讨医学图像背景分割的方法.方法 首先采用常规的自适应阈值方法对图像背景进行分割,但效果不理想;接着对医学图像的特点进行分析,最后采用背景拟合设定阈值进行分割.结果 实现了医学图像背景的分割.结论 实验表明上述方法能够非常有效地分割医学图像背景.     

基于医学图像的三维模拟手术

目的 对实现三维模拟手术的关键技术进行研究,构建基于医学图像的三维模拟手术平台. 方法在VC++平台下,使用三维分割算法对体数据进行分割,结合VTK对读入的体数据进行交互式三维重建和虚拟切割,并对各种虚拟切割方式的性能进行分析. 结果实现了医学CT/MR图像的交互式三维重建、虚拟手术刀切割以及虚拟手术规划. 结论该系统可以辅助医生对手术过程进行模拟,为医生观察三维人体组织器官结构及病灶部位,实施辅助手术提供有力的帮助.     

基于精确直方图规格化的医学超声图像增强

目的探求一种改进的精确直方图规格化方法,提高医学超声图像的对比度。方法针对医学超声图像的特点,引入一个有效的分割点,将原始图像分割为背景和前景区域,只对前景区域进行增强。结果本文方法将局部灰度拉伸到一个较大的动态范围,提高了对比度,抑制了医学超声图像背景过增强,保留了图像的细节信息。结论本文在增强图像对比度的同时能够有效地保留图像细节,是一种有效的对比度增强方法。     

图像分割在医学图像中的研究方法及应用

图像分割是指将一幅图像分解为若干互不交迭区域的集合,是图像处理与机器视觉的基本问题之一.医学图像分割是图像分割的一个重要应用领域,也是一个经典难题.本文从应用的特定角度,对近年来医学图像分割的新方法或改进算法进行综述,并简要讨论了每类分割方法的特点及应用.     

基于多重分形的医学图像分割方法

背景:由于人体解剖结构的复杂性、组织器官形状的不规则性及不同个体间的差异性,所以比较适合用多重分形来分析.目的:采用多重分形理论对医学图像进行图像分割.方法:采用基于容量测度的多重分形谱计算及基于概率测度的多重分形谱计算方法对图像进行分割.对于待处理图片分别进行传统的区域生长分割,max容量测度图像分割,sum容量测度图像分割,概率测度图像分割等4种分割,并加入噪声后再进行同样的分割处理作为比较.结果与结论:采用的两种基于多重分形谱的计算法中,基于容量测量的多重分形谱计算方法的关键是定义合适的测度μα;基于概率测度的多重分形谱计算方法的关键是定义合适的归一化概率Pi,不同的测度(概率)和不同的阈值对结果的影像比较大.基于概率测度的方法对噪声比较敏感,但是在滤过噪声时对图像象素大小变化比较大、比较复杂的图像有较好的分割效果.实验表明基于多重分形谱的医学图像分割方法在选择合适的测度(概率)和阈值时是可行的,特别是在较为复杂的图像处理中对于纹理和边缘的区别上有较大的优势,在准确地分割的同时能保留更多的细节,具有重要的实际意义.同时,多重分形也可以作为一种图像的特征,为特征提取多提供一种有力的数据.     

基于小波的磁共振图像聚类分割

目的 利用直方图自适应确定人体不同部位MRI的聚类类别的数目和相应的初始聚类中心,实现模糊-c均值聚类算法(FCM)分割的自适应。方法 首先采用小波变换拟合直方图的平滑包络线,降低噪声对寻找包络线极值的影响;其次根据微积分的知识求出包络线极大值的个数,按照文中给出的法则对包络线的极大值进行筛选,确定直方图中峰值的个数;最后以直方图中峰值的个数为聚类类别数,以相应的峰值为初始聚类中心,对MRI进行FCM分割。结果 采用该方法对多幅腹部和脑部MR图像进行分割,均能有效地自适应确定聚类的个数。结论 本文方法能够有效、准确地确定不同MR图像的聚类类别的个数,实现FCM的自适应。     

A hybrid approach for segmentation of anatomic structures in medical images

Objective

We propose a hybrid interactive approach for the segmentation of anatomic structures in medical images with higher accuracy at lower user interaction cost.

Materials and methods

Eighteen brain MR scans from the Internet Brain Segmentation Repository are used for brain structure segmentation. A MR scan and a CT scan of an old female are used for orbital structure segmentation. The proposed approach combines shape-based interpolation, radial basis function (RBF)-based warping and model-based segmentation. With this approach, to segment a structure in a 3D image, we first delineate the structure in several slices using interactive methods, and then use shape-based interpolation to automatically generate an initial 3D model of the structure from the segmented slices. To refine the initial model, we specify a set of additional points on the structure boundary in the image, and use a RBF to warp the model so that it passes the specified points. Finally, we adopt a point-anchored active surface approach to further deform the model for a better fitting of the model with its corresponding structure in image.

Results

Two brain structures and 15 orbital structures are segmented. For each structure, it needs only to semi- automatically segment three to five 2D slices and specify two to nine additional points on the structure boundary. The time cost for each structure is about 1–3 min. The overlap ratio of the segmentation results and the ground truth is higher than 96%.

Conclusion

The proposed method for the segmentation of anatomic structure achieved higher accuracy at lower user interaction cost, and therefore promising in many applications such as surgery planning and simulation, atlas construction, and morphometric analysis of anatomic structures.     

一种数字人脑部切片图像分割新方法

目的 提出一种人脑切片图像自动分割算法,以克服现有的方法对大量人工参与的依赖.方法 针对人脑切片图像的特征,提出一种基于区域生长的灰度直方图阈值化分割算法.首先通过区域生长过程对图像进行初始的粗分割,再用直方图阈值化方法进行二次细分割提取目标区域.结果 采用此方法准确有效地分割出了大脑白质和大脑皮质.结论 此算法结合切片图像的全局信息和局部信息应用于分割,是一种比较好的分割方法.     

Suggestive annotation of brain MR images with gradient-guided sampling

Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of machine learning, in particular supervised learning, depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire, it takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain MR images that can suggest informative sample images for human experts to annotate. We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation. Experiments show that for brain tumour segmentation task on the BraTS 2019 dataset, training a segmentation model with only 7% suggestively annotated image samples can achieve a performance comparable to that of training on the full dataset. For whole brain segmentation on the MALC dataset, training with 42% suggestively annotated image samples can achieve a comparable performance to training on the full dataset. The proposed framework demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications.     

Multiatlas Fusion with a Hybrid CT Number Correction Technique for Subject-Specific Pseudo-CT Estimation in the Context of MRI-Only Radiation Therapy

ObjectiveTo propose a hybrid multiatlas fusion and correction approach to estimate a pseudo–computed tomography (pCT) image from T2-weighted brain magnetic resonance (MR) images in the context of MRI-only radiotherapy.Materials and MethodsA set of eleven pairs of T2-weighted MR and CT brain images was included. Using leave-one-out cross-validation, atlas MR images were registered to the target MRI with multimetric, multiresolution deformable registration. The subsequent deformations were applied to the atlas CT images, producing uncorrected pCT images. Afterward, a three-dimensional hybrid CT number correction technique was used. This technique uses information about MR intensity, spatial location, and tissue label from segmented MR images with the fuzzy c-means algorithm and combines them in a weighted fashion to correct Hounsfield unit values of the uncorrected pCT images. The corrected pCT images were then fused into a final pCT image.ResultsThe proposed hybrid approach proved to be performant in correcting Hounsfield unit values in terms of qualitative and quantitative measures. Average correlation was 0.92 and 0.91 for the proposed approach by taking the mean and the median, respectively, compared with 0.86 for the uncorrected unfused version. Average values of dice similarity coefficient for bone were 0.68 and 0.72 for the fused corrected pCT images by taking the mean and the median, respectively, compared with 0.65 for the uncorrected unfused version indicating a significant bone estimation improvement.ConclusionA hybrid fusion and correction method is presented to estimate a pCT image from T2-weighted brain MR images.     

一种基于人脑三维模型分割断面图像上脑沟、回的方法与实现

目的 借助人脑三维模型实现二维断面图像上大脑沟、回的分割。方法 首先在三维脑模型上以勾勒轮廓的方式界定不同脑沟、脑回区域,然后映射到断面相应区域上,进行区域内颜色填充,达到分割目的;并采用Visual C++ 6.0结合可视化类库工具包搭建脑沟、回分割平台,予以实现。结果 准确有效地分割出了序列断面图像上的右脑中央前回和中央后回。结论 此方法为获取完整、连续的脑沟、脑回断面解剖图谱提供了一种简单可行的实现手段,对于丰富数字化脑图谱及促进脑部功能与疾病诊断定位相关研究有重要意义。     

基于MeanShift方法的肝脏CT图像的自动分割

目的 探讨基于Mean Shift方法的肝脏CT图像的自动分割算法,以实现肝脏的自动分割。方法 首先对原始图像进行单次Mean Shift平滑 ,滤除噪声的影响以增强算法的鲁棒性,然后通过Mean Shift迭代自动选取初始种子点,最后采用基于区域生长的方法实现肝脏CT图像的自动分割。结果 实验证明此方法是一个准确、快速和有效的肝脏自动分割方法。结论 采用本文中提出的方法,可有效地实现肝脏的自动分割。     

Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease

Purpose

Template-based segmentation techniques have been developed to facilitate the accurate targeting of deep brain structures in patients with movement disorders. Three template-based brain MRI segmentation techniques were compared to determine the best strategy for segmenting the deep brain structures of patients with Parkinson’s disease.

Methods

T1-weighted and T2-weighted magnetic resonance (MR) image templates were created by averaging MR images of 57 patients with Parkinson’s disease. Twenty-four deep brain structures were manually segmented on the templates. To validate the template-based segmentation, 14 of the 24 deep brain structures from the templates were manually segmented on 10 MR scans of Parkinson’s patients as a gold standard. We compared the manual segmentations with three methods of automated segmentation: two registration-based approaches, automatic nonlinear image matching and anatomical labeling (ANIMAL) and symmetric image normalization (SyN), and one patch-label fusion technique. The automated labels were then compared with the manual labels using a Dice-kappa metric and center of gravity. A Friedman test was used to compare the Dice-kappa values and paired t tests for the center of gravity.

Results

The Friedman test showed a significant difference between the three methods for both thalami (p < 0.05) and not for the subthalamic nuclei. Registration with ANIMAL was better than with SyN for the left thalamus and was better than the patch-based method for the right thalamus.

Conclusion

Although template-based approaches are the most used techniques to segment basal ganglia by warping onto MR images, we found that the patch-based method provided similar results and was less time-consuming. Patch-based method may be preferable for the subthalamic nucleus segmentation in patients with Parkinson’s disease.