多发性硬化症MR图像分割新算法研究

提出了一种针对多发性硬化症病灶T2加权脑部磁共振（MR）图像的分割算法。根据多发性硬化症病灶和脑脊液在T2加权像上同表现为高亮度信号的特点,把模糊C均值分割算法与形态学方法相结合,提出了基于核模糊C均值的多发性硬化症病灶分割算法。该算法首先用改进的核模糊C均值算法做基础分割,再用形态学方法提取出多发性硬化症病灶得到最终分割结果。通过对多发性硬化症模拟脑部MR图像的分割结果表明,算法能够比较准确地分割多发性硬化症病灶。     

基于D-S证据理论的多发性硬化症病灶分割算法*

多发性硬化症是一种严重威胁中枢神经功能的疾病,对其病灶自动检测方法的研究正受到越来越多的关注.基于D-S证据理论和模糊C-均值(FCM)聚类算法,提出了一种融合T1和T2加权MR图像信息的多发性硬化症自动分割算法.首先运用FCM聚类算法分别分割T1和T2加权MR图像,然后利用根据D-S证据理论得到的融合两种加权图像信息...     

基于 CT 和磁共振 T2 加权图像双模态分类模型的 自发性脑出血后脑水肿在 CT 图像上的分割

自发性脑出血后脑水肿在 CT 图像呈现的模糊边缘是 CT 图像上实现脑水肿自动分割的一个严峻挑战。在磁共振 T2加权图像上,脑水肿的边界相对清晰。因此,文章提出利用14套同时拥有磁共振和 CT 图像的病例,将其磁共振 T2加权图像的手动分割金标准通过配准映射到 CT 空间,结合 CT 图像信息通过对配准后的结果进行机器学习得到脑水肿体素分类器,并利用此分类器进行 CT 图像上的脑水肿分割。采用近邻采样策略,选择公共测度子空间进行特征选择,基于支持向量机方法利用穷举法得到分割精度最高的水肿分类器;通过36套临床脑出血的 CT 数据的验证,结果显示该方法的 Dice 系数达到0.859±0.037,明显高于基于区域增长的方法(0.789±0.036,P＜0.0001)、半自动水平集方法(0.712±0.118,P＜0.0001)和基于阈值的方法(0.649±0.147,P＜0.0001)。与之对比,使用 CT 手动分割金标准得到的分类器分割精度 Dice 系数(0.686±0.136,P＜0.0001)明显小于基于 T2金标准的分类器。试验结果显示磁共振 T2加权图像上脑水肿的清晰边界在精确区分水肿与周围正常脑组织的时候可能提供更强的约束。文章提出的方法为脑出血患者的脑水肿量化、病理改变严重性的评估、以及治疗提供潜在的工具。     

一种快速提取连续CT脑肿瘤病灶区的算法

脑肿瘤图像提取就是将肿瘤病灶区域(水肿、坏死、癌变)从正常的脑部组织(灰质、白质、脑脊液)分开,精确的脑肿瘤分割对脑瘤的诊断、研究和治疗有重要的临床意义;针对传统脑部CT肿瘤病灶提取的缺点,即需要耗费大量时间并且分割精度不高的问题,提出一种综合了形态学重建、分水岭分割和改进的区域生长算法;先用形态学重建进行去噪,再用结合多尺度梯度分水岭分割提取整个图像的边界,然后在肿瘤病灶区域内选取种子点进行区域生长,提取肿瘤区域轮廓,滤除其他封闭区域,得到的图像作为改进的区域生长法的初始分割区域,使用改进的区域生长法,滤除过分割区域;实验结果显示该算法分割出的结果有效区域大,分割精度高;该算法提高了分割精度,由于不用匹配结构参数,加快了分割速度,具有一定的临床价值。     

一种CT医学图像分割新算法

针对临床辅助诊断的需要,提出了一种结合传统分水岭算法和新型核聚类算法的CT医学图像分割新算法。首先,通过分水岭变换,CT图被分割成不同的小区域。然后,根据改进的KFCM算法,利用Mercer核将各个小区域的平均灰度值映射到高维特征空间,使得原来在分水岭算法分割图中未显示出来的特征显现出来。通过此方法,相较于传统核聚类(KFCM)算法,我们可实现更准确的聚类,并有效解决分水岭算法分割CT医学图像的过分割问题,因此能取得更好的分割效果。实验结果显示,本文方法能够很好分割腹腔CT图,获得更清晰的分割图像。     

基于运动信息与多尺度分水岭的视频目标分割

从视频序列中提取视频目标是基于内容编码中的一项关键技术。提出了将高阶统计运动检测和多尺度分水岭相结合的视频目标分割算法。该算法首先利用高阶统计运动检测算法检测出运动区域,通过后处理得到运动目标的初始模板。然后,用小波变换对视频图像进行多分辨率分解。在最低分辨率上应用分水岭算法分割得到具有精确边缘的分割区域,通过将区域融合后的区域逐步投影到高分辨率图像上并结合高分辨率图像上的分水岭算法逐步提取出具有精确边缘的区域。最后,将运动目标的初始模板和多尺度分水岭分割得到的区域结合起来提取出具有精确边缘的视频对象。实验结果表明该算法能有效地分割和提取出视频序列中的视频对象。     

基于PSO_KFCM的医学图像分割

在核模糊聚类算法(KFCM)的基础上,提出了一种新的PSO KFCM聚类算法.新算法利用高斯核函数,把输入空间的样本映射到高维特征空间,利用微粒群算法的全局搜索、快速收敛的特点,代替KFCM算法逐次迭代的过程,在特征空间中进行聚类,克服了KFCM对初始值和噪声数据敏感、易陷入局部最优的缺点.通过对医学图像进行分割,仿真实验结果表明,新算法在性能上比KFCM聚类算法有较大改进,具有更好的聚类效果,且算法能够很快地收敛.     

一种新的视频图像分割算法

针对传统水平集需要不断重新初始化,不能直接应用到视频图像分割中的问题,提出一种新的视频图像分割算法。该算法根据图像序列中相邻的2帧,利用帧差法进行前期处理,并经过二元休憩和数学形态学的处理得到一个运动目标的模板,将该模板作为新的初始水平集进行演化,可以得到图像序列中的运动目标。实验结果表明,该算法能够得到运动目标的准确位置,避免传统水平集需要不断重新初始化的问题,减少计算复杂度。     

结合空间-光谱调制及图像分割的多光谱图像融合方法

为了进一步提高多光谱(MS)图像与全色(PAN)图像之间的融合质量,平衡空间细节的注入与光谱信息的保持,提出了一种基于局部自适应空间-光谱调制与图像协同分割的融合方法.该方法利用k-means算法、根据MS图像的光谱特性进行图像分割,得到不同的连通体组,进而基于局部连通体组构建了局部自适应光谱调制(LASpeM)系数和局部自适应空间调制(LASpaM)系数,分别对融合图像中的光谱与空间信息进行调制;其中,LASpeM系数的构建基于MS和PAN图像中的细节提取以及MS波段之间的光谱关系, LASpaM系数的构建则基于MS和低分辨率PAN图像之间光谱特性的局部差异及相关性.另外,引入融合与分割的协同思想,利用图像分割来优化融合结果,并根据融合结果的反馈信息对分割算法的参数进行调整.在Matlab环境下,采用2个卫星GeoEye-1和QuickBird数据集进行融合实验,结果表明,文中方法在主观视觉与客观评价指标方面总体上优于7种经典及流行的融合方法,能够平衡融合图像的空间信息注入和光谱信息保持,有效地减少光谱扭曲.     

基于运动信息与马尔可夫彩色聚类的运动目标分割

针对视频序列图像中的运动目标分割,论文提出了将运动检测和马尔可夫彩色聚类相结合的运动目标分割算法。该算法首先利用基于统计模型的运动检测算法,通过后处理,得到运动目标的初始模板。然后,利用区域生长算法进行彩色图像的初始分割,在初始分割的基础上应用马尔可夫随机场模型进行彩色聚类,得到具有精确边缘的分割区域。最后,将运动目标的初始模板和彩色精确分割结合起来提取出具有精确边缘的运动目标。实验结果表明该算法能有效地分割和提取出视频序列中的运动目标。     

Topology correction of segmented medical images using a fast marching algorithm

We present here a new method for correcting the topology of objects segmented from medical images. Whereas previous techniques alter a surface obtained from a binary segmentation of the object, our technique can be applied directly to the image intensities of a probabilistic or fuzzy segmentation, thereby propagating the topology for all isosurfaces of the object. From an analysis of topological changes and critical points in implicit surfaces, we derive a topology propagation algorithm that enforces any desired topology using a fast marching technique. The method has been applied successfully to the correction of the cortical gray matter/white matter interface in segmented brain images and is publicly released as a software plug-in for the MIPAV package.     

White matter hyper-intensities automatic identification and segmentation in magnetic resonance images

A methodology for automatic identification and segmentation of white matter hyper-intensities appearing in magnetic resonance images of brain axial cuts is presented. To this end, a sequence of image processing technics is employed to form an image where the hyper-intensities in white matter differ notoriously from the rest of the objects. This pre-processing stage facilitates the posterior process of identification and segmentation of the hyper-intensity volumes. The proposed methodology was tested on 55 magnetic resonance images from six patients. These images were analysed by the proposed system and the resulted hyper-intensity images were compared with the images manually segmented by experts. The experimental results show the mean rate of true positives of 0.9, the mean rate of false positives of 0.7 and the similarity index of 0.7; it is worth commenting that the false positives are found mostly within the grey matter not causing problems in early diagnosis. The proposed methodology for magnetic resonance image processing and analysis may be useful in the early detection of white matter lesions.     

改进FCM和LFP相结合的白细胞图像分类

研究白细胞图像分类识别中有效的图像分割与特征提取方法,以提高白细胞图像的正确识别率.由于某些白细胞(粒细胞)中颗粒的存在,严重影响细胞核与细胞质区域的正确分割,通过将空间信息与核函数融入模糊C-均值聚类(FCM)算法,提出一种改进的FCM算法.应用该算法对白细胞图像进行分割,并采用数学形态学方法对分割后的图像进行处理,获得了很好的分割效果,解决了粒细胞的质核分割难题.对于细胞的纹理特征提取,通过对局部二值模式(LBP)中阈值参数的模糊化,建立了基于局部模糊模式(LFP)的纹理特征提取算法.运用本文方法进行图像分割和纹理提取,以支持向量机作为分类器,对CellAtlas的100幅白细胞图像进行了分类识别的实验,结果表明白细胞的正确识别率达到93％.     

基于小波变换和KFCM的彩色图像分割

提出一种将小波变换和核模糊C均值聚类算法相结合的快速彩色图像分割算法。利用小波变换的多分辨率特性,在分辨率最大尺度上的LL子带进行均值漂移聚类,快速获得初始粗分割结果,在其基础上进行模糊核聚类分割,将上一层的结果用于下一层的初始化,重复至最低分辨率后用最小分类器对原始图像进行最终分割。实验结果证明,该算法分割速度快,对自然彩色图像的分割结果优于模糊C均值算法和均值漂移算法。     

Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation

This study presents an image segmentation system that automatically segments and labels T1-weighted brain magnetic resonance (MR) images. The method is based on a combination of unsupervised learning algorithm of the self-organizing maps (SOM) and supervised learning vector quantization (LVQ) methods. Stationary wavelet transform (SWT) is applied to the images to obtain multiresolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. A multidimensional feature vector is formed by combining SWT coefficients and their statistical features. This feature vector is used as input to the SOM. SOM is used to segment images in a competitive unsupervised approach and an LVQ system is used for fine-tuning. Results are evaluated using Tanimoto similarity index and are compared with manually segmented images. Quantitative comparisons of our system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that our system shows better segmentation performance for the gray matter while it gives average results for white matter.     

Multiscale Segmentation of Three-Dimensional MR Brain Images

Segmentation of MR brain images using intensity values is severely limited owing to field inhomogeneities, susceptibility artifacts and partial volume effects. Edge based segmentation methods suffer from spurious edges and gaps in boundaries. A multiscale method to MRI brain segmentation is presented which uses both edge and intensity information. First a multiscale representation of an image is created, which can be made edge dependent to favor intra-tissue diffusion over inter-tissue diffusion. Subsequently a multiscale linking model (the hyperstack) is used to group voxels into a number of objects based on intensity. It is shown that both an improvement in accuracy and a reduction in image post-processing can be achieved if edge dependent diffusion is used instead of linear diffusion. The combination of edge dependent diffusion and intensity based linking facilitates segmentation of grey matter, white matter and cerebrospinal fluid with minimal user interaction. To segment the total brain (white matter plus grey matter) morphological operations are applied to remove small bridges between the brain and cranium. If the total brain is segmented, grey matter, white matter and cerebrospinal fluid can be segmented by joining a small number of segments. Using a supervised segmentation technique and MRI simulations of a brain phantom for validation it is shown that the errors are in the order of or smaller than reported in literature.     

A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches

This paper presents a novel idea of intracranial segmentation of magnetic resonance (MR) brain image using pixel intensity values by optimum boundary point detection (OBPD) method. The newly proposed (OBPD) method consists of three steps. Firstly, the brain only portion is extracted from the whole MR brain image. The brain only portion mainly contains three regions–gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). We need two boundary points to divide the brain pixels into three regions on the basis of their intensity. Secondly, the optimum boundary points are obtained using the newly proposed hybrid GA–BFO algorithm to compute final cluster centres of FCM method. For a comparison, other soft computing techniques GA, PSO and BFO are also used. Finally, FCM algorithm is executed only once to obtain the membership matrix. The brain image is then segmented using this final membership matrix. The key to our success is that we have proposed a technique where the final cluster centres for FCM are obtained using OBPD method. In addition, reformulated objective function for optimization is used. Initial values of boundary points are constrained to be in a range determined from the brain dataset. The boundary points violating imposed constraints are repaired. This method is validated by using simulated T1-weighted MR brain images from IBSR database with manual segmentation results. Further, we have used MR brain images from the Brainweb database with additional noise levels to validate the robustness of our proposed method. It is observed that our proposed method significantly improves segmentation results as compared to other methods.