《多分辨率水平集算法的乳腺MR图像分割》
针对乳腺 MR 图像信息量大、灰度不均匀、边界模糊、难分割的特点, 提出一种多分辨率水平集乳腺 MR图像分割算法. 算法的核心是首先利用小波多尺度分解对图像进行多尺度空间分析, 得到粗尺度图像; 然后对粗尺度图像利用改进 CV 模型进行分割. 为了去除乳腺 MR 图像中灰度偏移场对分割效果的影响, 算法中引入局部拟合项, 并用核函数进一步改进 CV模型, 进而对粗尺度分割效果进行优化处理. 仿真和临床数据分割结果表明, 所提算法分割灰度不均匀图像具有较高的分割精度和鲁棒性, 能够有效的实现乳腺 MR 图像的分割。
《三维肝脏MR图像分割技术研究》
医学影像学发展至今，已经广泛地应用于临床医学的各个相关邻域。利用合适的图像处理算法对医学图像进行相应的处理，能够对基于医学图像的诊断以及其他研究工作提供更加有效、便捷的信息，医学图像的分割在医学图像处理中占据着重要的位置。从医学影像中可通过分割算法提取出感兴趣区域并予以单独显示，能够更加直观地提供病变或正常组织结构信息，并且分割的结果可以应用在为一定目的而进行的后续处理当中，比如图像配准、目标组织的定量测量等。 磁共振成像技术在当前医学研究与临床诊疗中发挥着愈加重要的作用，与其他成像方式相比，MRI对软组织和内脏的成像能力高，能够非常清晰的显示人体组织解剖结构，并具有多参数（T1、T2等）、多方位成像的优点。MR图像的成像效果很好地区分了各个组织，在此基础上可以对感兴趣区域进行更为直观地分割。近年来国民肝部病变的多发使得基于腹部扫描图像的肝脏分割成为亟待解决的问题，然而人体腹部包含大量脏器及软组织，结构复杂，并且脏器与软组织之间的粘连导致成像结果中存在浸润现象，从而形成大量弱边缘和伪边缘，这使得面向内脏的分割非常困难。再加上磁共振成像过程较为复杂，成像效果存在一定的不确定性，不同的组织器官之间广泛存在的差异性，准确地从腹部MR扫描图像中提取出肝脏具有重要的理论意义以及应用价值。 本文系统的分析了当前应用于医学图像分割的多种算法，对它们的优劣势以及应用范围进行了比较和总结。根据腹部图像的特点选取水平集算法对肝脏进行提取，详细描述了水平集算法的原理、特征，以及发展至今研究人员对其进行的各种改进和应用。由于人体结构的复杂性和个体之间的差异性，图像分割算法发展至今仍然没有一种单一的方法对人体各个部位达到有效的分割，当前主要的研究方向是综合多种算法的优点，结合目标分割区域的形态特征进行混合分割。本文课题就是在此前提下分析考量了多种算法并研究了人体肝脏的形态特征和成像特点之后，选用阈值分割算法与水平集结合的方式，并加入一些其他的算法进行辅助分割，较好的实现了三维腹部图像肝脏的提取工作。 本文主要研究工作如下： 一、首先将从医学影像设备中获取的序列切片图像根据扫描间隔和切片层厚进行堆叠，为使其更加接近真实人体数据在切片间进行插值，为保证数据的真实性插值的数据尽量减小。 二、对插值后的体数据进行降噪滤波，由于水平集算法对图像边界信息敏感，要尽量保持图像中的边缘，采用高斯滤波或各向异性扩散滤波均可达到良好的效果。 三、使用阈值分割与水平集结合对肝脏进行提取，并在此步骤中加入非线性映射，在增强图像的同时产生良好的速度图像，使得分割结果中的演化溢出现象得以避免。 四、结合可视化工具包VTK使用光线投射算法对分割结果以及中间步骤各个算法的处理效果进行三维重建。 实验结果表明本课题所选用的算法结合方式获得了较为理想的分割效果，很好的将水平集算法应用到了三维肝脏的分割工作当中，有效的避免了水平集算法在弱边缘处泄露的问题，为针对肝脏的后续研究提供了基础。
《结合非局部均值的快速FCM算法分割MR图像研究》
针对FCM算法分割医学MR图像存在的运算速度慢、对初始值敏感以及难以处理MR图像中固有Rician噪声等缺陷,提出了一种结合非局部均值的快速FCM算法。该算法的核心是首先针对MR图像中存在的Rician噪声,利用非局部均值算法对图像进行去噪处理,消除噪声对分割结果的影响；然后根据所提出的新的自动获取聚类中心的规则得到初始聚类中心；最后将得到的聚类中心作为快速FCM算法的初始聚类中心用于去噪后的图像分割,解决了随机选择初始聚类中心造成的搜索速度慢和容易陷入局部极值的问题。实验表明,该算法能够快速有效地分割图像,并且具有较好的抗噪能力。
《MR图像中的肝脏分割和肿瘤提取》
磁共振MR(Magnetic Resonance)图像是公认的确认肝脏有无肿瘤等器质性病变的金标准检查方法,其中涉及肝脏的分割以及肿瘤的提取.由于脏器组织浸润和个体差异,在解决肝脏分割和肿瘤提取方面还没有通用的数字图像处理方法.在现有研究的基础上,以迭代四叉树(IQD)自动分割算法和基于灰度的分割方法,实现MR图像中肝脏的自动分割和肿瘤的提取.实验结果表明,这一套方法的可行性和优势.
《基于图划分的形状统计主动轮廓模型心脏MR图像分割》
为有效分析心脏功能，高精度分割左、右心室是必要的.心脏MR图像中存在图像灰度不均，左、右心室及周围其它组织灰度接近，存在弱边缘、边缘断裂及噪声造成边缘模糊等现象，给精确分割左、右心室轮廓带来困难.本文在基于图划分的主动轮廓方法基础上，通过对训练形状进行配准及变化模式分析，定义左、右心室轮廓形状变化允许空间，提出基于图划分的形状统计主动轮廓模型来分割心脏MR图像.该方法通过图划分理论将图像分割问题转化为最优化问题，所以能够得到全局最优解，具有较大的捕捉范围.还引入形状统计来引导曲线的演化，有效处理曲线演化时存在的边缘泄漏问题，提高分割精度.实验结果表明，本文方法较以往方法具有更高的分割精度和更好的稳定性，为临床应用提供一种较可行的方法.
《Cardiac MR Image Segmentation Techniques: an overview》
Broadly speaking, the objective in cardiac image segmentation is to delineate the outer and inner walls of the heart to segment out either the entire or parts of the organ boundaries. This paper will focus on MR images as they are the most widely used in cardiac segmentation – as a result of the accurate morphological information and better soft tissue contrast they provide. This cardiac segmentation information is very useful as it eases physical measurements that provides useful metrics for cardiac diagnosis such as infracted volumes, ventricular volumes, ejection fraction, myocardial mass, cardiac movement, and the like. But, this task is difficult due to the intensity and texture similarities amongst the different cardiac and background structures on top of some noisy artifacts present in MR images. Thus far, various researchers have proposed different techniques to solve some of the pressing issues. This seminar paper presents an overview of representative medical image segmentation techniques. The paper also highlights preferred approaches for segmentation of the four cardiac chambers: the left ventricle (LV), right ventricle (RV), left atrium (LA) and right atrium (RA), on short axis image planes.
《MR Image Segmentation of Left Ventricle Based on the Multi-information Gaussian Mixture Model》
The Level set method has consequence in the fields of image segmentations.As the traditional active contour methods only use the information of the edge,when it segments images with strong noise or with weak edges it is difficult to get the true edge.Gaussian mixture model uses the global information of the image,so it can do solve the problems of the weak edges.But the traditional Gaussian mixture model only uses the information of the histogram and not uses the information of the location of the pixel.So it is sensitive to the noise.This paper gives a method to make a new information field,which combines the information of the region,texture and region simulation.With the new information field the Gaussian mixture model can reduce the effect of the noise.In this paper the Gaussian mixture model is introduced to the Level set model and reduces the effect of the noise and prevents the curve over the weak edges.After getting the inner edge of the left ventricle,this paper uses the region and shape information to segment the out edge.Experiments on the segmentation of left ventricle magnetic resonance images show this model has better effect in image segmentation.
《Prostate MR image segmentation using 3D Active Appearance Models》
This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain the point correspondence between the given training cases. Subsequently, an AAM is used to segment the prostate on 50 training cases. The method is evaluated using a 5-fold cross validation over 5 repetitions. The mean Dice similarity coefficient and 95% Hausdorff distance are 0.78 and 7.32 mm respectively. Prostate segmentation is essential for calculating prostate volume, image fusion, creating patient-specific prostate anatomical models, and as a pre-processing step for many computer aided diagnosis algorithms. Furthermore, information about the size, volume, shape and location of the prostate relative to adjacent organs is an essential part of planning for minimally invasive therapies and biopsies. Because manual segmentation of the prostate is time-consuming and highly subjective, (semi-)automatic segmentation methods are preferable. However, segmenting the prostate in MR images is challenging due to the large variations of prostate shape between subjects, the lack of clear prostate boundaries and the similar intensity profiles of the prostate and surrounding tissues. The 2012 MICCAI challenge: " Prostate MR Image Segmentation " involves segmentation of the prostate on transversal T2-weighted images. The goal of the challenge is to evaluate segmentation algorithms on images from multiple centers and multiple MRI device vendors. Only a few prostate segmentation methods for T2-weighted MR images currently exist. Klein et al. [1] proposed a method based on non-rigid registration of a set of pre-labeled atlas images, against the target patients image, using mutual information. Subsequently, the segmentation is obtained as the average of the best matched registered atlas sets. Multiple modifications are published on this atlas based prostate segmentation method [2–4]. The methods presented by Toth et al. [5] and Ghose et al. [6, 7] are based on statistical shape models. Toth et al. used a levelset-based statistical shape。
《A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features》
Blurred boundaries and heterogeneous intensities make accurate prostate MR image segmentation problematic. To improve prostate MR image segmentation we suggest an approach that includes: (a) an image patch division method to partition the prostate into homogeneous segments for feature extraction; (b) an image feature formulation and classification method, using the relevance vector machine, to provide probabilistic prior knowledge for graph energy construction; (c) a graph energy formulation scheme with Bayesian priors and Dirichlet graph energy and (d) a non-iterative graph energy minimization scheme, based on matrix differentiation, to perform the probabilistic pixel membership optimization. The segmentation output was obtained by assigning pixels with foreground and background labels based on derived membership probabilities. We evaluated our approach on the PROMISE-12 dataset with 50 prostate MR image volumes. Our approach achieved a mean dice similarity coefficient (DSC) of 0.90 ± 0.02, which surpassed the five best prior-based methods in the PROMISE-12 segmentation challenge.
《Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method》
In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.