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索的模糊隶属度取值,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MRT1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。     

模糊K-均值聚类算法及其在磁共振颅脑图像分割中的应用研究

目的　介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。方法　首先对磁共振颅脑图像进行预处理去掉颅骨和肌肉等非脑组织,只保留大脑组织,然后利用模糊Ｋ－ 均值聚类算法计算脑白质、脑灰质和脑脊液的模糊类属函数。结果　模糊Ｋ－ 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、白质和脑脊液。结论　利用模糊Ｋ－ 均值聚类算法分割磁共振颅脑图像能获得较好的分割效果。     

点对称距离模糊C均值聚类算法在脑部MRI图像分割中的应用

背景：在传统的图像分割方法中,模糊C均值聚类算法应用十分广泛。目的：将改进的模糊C均值聚类算法应用到MRI图像的分割中,提高MRI图像分割的准确度。方法：针对传统的基于Minkowski距离的模糊C均值聚类算法,提出了基于点对称距离的模糊C均值聚类算法,并将其运用到了脑部MRI图像分割中。结果与结论：实验结果表明,与模糊C均值聚类算法相比,点对称距离的模糊C均值聚类算法有明显的优势。     

点对称距离模糊C均值聚类算法在脑部MRI图像分割中的应用

背景:在传统的图像分割方法中,模糊C均值聚类算法应用十分广泛。目的:将改进的模糊C均值聚类算法应用到MRI图像的分割中,提高MRI图像分割的准确度。方法:针对传统的基于Minkowski距离的模糊C均值聚类算法,提出了基于点对称距离的模糊C均值聚类算法,并将其运用到了脑部MRI图像分割中。结果与结论:实验结果表明,与模糊C均值聚类算法相比,点对称距离的模糊C均值聚类算法有明显的优势。     

模糊K—均聚类算法及其在磁共振颅脑图像分割中的应用研究

目的 介绍一种动态模糊聚类算法并和该算法对磁共振图像进行分割研究。方法 首先对磁共振颅脑图像进行预处理去掉颅骨和肌肉等非脑组织,只保留大脑组织,然后利用模糊Ｋ－均值聚类算法计算脑白质、脑灰质和脑脊液的模糊类属函数。结果 模糊Ｋ－均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、白质和脑脊液。结论 利用模糊Ｋ－均值聚类算法分割磁共振颅脑图像能获得较好的分割效果。     

蚁群算法在磁共振图像分割中的应用

研究一种智能的图像分割方法并且把这种分割方法应用到磁共振的图像分割中,对目前应用的图像分割方法进行比较后提出了一种基于蚁群的磁共振图像分割方法。最后将算法应用到颅脑磁共振的图像分割当中,实验结果表明新算法具有很强的噪声和模糊边界的检测能力。该算法的提出对磁共振研究和临床应用都有很大的理论和实践意义。     

视觉诱发电位在多发性硬化症中的应用价值

目的：研究视觉诱发电位(vsual evoked potential，VEP)在多发性硬化症(multiple sclerosis，MS)中的临床应用价值。方法:应用棋盘格翻转方式分别对27例明确为MS患者或可疑患者(存在缓解复发特点)和27例健康成人进行VEP检测，将测量结果进行比较，与临床症状体征进行对照分析。结果:可疑诊断或明确诊断MS的患者中，有相应视神经症状者13例，异常率为85％(11／13)，无症状者14例，异常率79％(11／14)，差异有显著性意义。结论:VEP为早期诊断或发现亚临床病灶提供了病灶空间表现的依据。     

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.     

视觉诱发电位在多发性硬化症中的应用价值

目的研究视觉诱发电位(vsualevokedpotential,VEP)在多发性硬化症(multiplesclerosis,MS)中的临床应用价值。方法应用棋盘格翻转方式分别对27例明确为MS患者或可疑患者(存在缓解复发特点)和27例健康成人进行VEP检测,将测量结果进行比较,与临床症状体征进行对照分析。结果可疑诊断或明确诊断MS的患者中,有相应视神经症状者13例,异常率为85%(11/13),无症状者14例,异常率79%(11/14),差异有显著性意义。结论VEP为早期诊断或发现亚临床病灶提供了病灶空间表现的依据。     

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。     

Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients

Multiple sclerosis (MS) affects both white matter and gray matter (GM). Measurement of GM volumes is a particularly useful method to estimate the total extent of GM tissue damage because it can be done with conventional magnetic resonance images (MRI). Many algorithms exist for segmentation of GM, but none were specifically designed to handle issues associated with MS, such as atrophy and the effects that MS lesions may have on the classification of GM. A new GM segmentation algorithm has been developed specifically for calculation of GM volumes in MS patients. The new algorithm uses a combination of intensity, anatomical, and morphological probability maps. Several validation tests were performed to evaluate the algorithm in terms of accuracy, reproducibility, and sensitivity to MS lesions. The accuracy tests resulted in error rates of 1.2% and 3.1% for comparisons to BrainWeb and manual tracings, respectively. Similarity indices indicated excellent agreement with the BrainWeb segmentation (0.858–0.975, for various levels of noise and rf inhomogeneity). The scan-rescan reproducibility test resulted in a mean coefficient of variation of 1.1% for GM fraction. Tests of the effects of varying the size of MS lesions revealed a moderate and consistent dependence of GM volumes on T2 lesion volume, which suggests that GM volumes should be corrected for T2 lesion volumes using a simple scale factor in order to eliminate this technical artifact. The new segmentation algorithm can be used for improved measurement of GM volumes in MS patients, and is particularly applicable to retrospective datasets.     

Segmentation of brain tissue from magnetic resonance images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. The brain is a particularly complex structure, and its segmentation is an important step for many problems, including studies in temporal change detection of morphology, and 3-D visualizations for surgical planning. We present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the computer vision literature: expectation/maximization segmentation, binary mathematical morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation such that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256 × 256 × 124 voxels and validate those results against segmentations generated by neuroanatomy experts.     

A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions

We describe a new fully automatic method for the segmentation of brain images that contain multiple sclerosis white matter lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas-based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing such as cortical unfolding or diffeomorphic shape analysis techniques. Evaluation with both simulated and real data sets demonstrates that the method has an accuracy competitive with state-of-the-art MS lesion segmentation methods, while simultaneously segmenting the whole brain.     

Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images

A local image model is proposed to eliminate the adverse impact of both artificial and inherent intensity inhomogeneities in magnetic resonance imaging on intensity-based image segmentation methods. The estimation and correction procedures for intensity inhomogeneities are no longer indispensable because the highly convoluted spatial distribution of different tissues in the brain is taken into consideration. On the basis of the local image model, multicontext fuzzy clustering (MCFC) is proposed for classifying 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid automatically. In MCFC, multiple clustering contexts are generated for each pixel, and fuzzy clustering is independently performed in each context to calculate the degree of membership of a pixel to each tissue class. To maintain the statistical reliability and spatial continuity of membership distributions, a fusion strategy is adopted to integrate the clustering outcomes from different contexts. The fusion result is taken as the final membership value of the pixel. Experimental results on both real MR images and simulated volumetric MR data show that MCFC outperforms the classic fuzzy c-means (FCM) as well as other segmentation methods that deal with intensity inhomogeneities.     

多发性硬化灰质病变多模态MRI研究进展

近年来,多发性硬化(MS)灰质病变逐渐被人们所关注.利用多模态MRI可获得MS灰质病变的定性、定量信息,有助于早期发现灰质病变,且多模态MRI可用于监测MS的病程进展.本文对MS灰质病变的多模态MRI研究进展进行综述.     

Segmentation of tongue muscles from super-resolution magnetic resonance images

Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and swallowing problems.     

Segmentation of the mouse hippocampal formation in magnetic resonance images

The hippocampal formation plays an important role in cognition, spatial navigation, learning, and memory. High resolution magnetic resonance (MR) imaging makes it possible to study in vivo changes in the hippocampus over time and is useful for comparing hippocampal volume and structure in wild type and mutant mice. Such comparisons demand a reliable way to segment the hippocampal formation. We have developed a method for the systematic segmentation of the hippocampal formation using the perfusion-fixed C57BL/6 mouse brain for application in longitudinal and comparative studies. Our aim was to develop a guide for segmenting over 40 structures in an adult mouse brain using 30 μm isotropic resolution images acquired with a 16.4 T MR imaging system and combined using super-resolution reconstruction.     

Variant of multiple sclerosis with dementia and tumefactive demyelinating brain lesions

We describe an unusual clinical and diagnostic featureof a patient with multiple sclerosis(MS). A 25-yearold woman was admitted to the Neurology department(December 2009) with one month history of rapid cognitive deterioration. She had poor cognition, dysphasia, reduction in visual acuity and temporal pallor of the optic discs. She had prolonged latencies of P100 component of visual evoked potentials(VEPs). Magnetic resonance imaging(MRI)-brain showed multifocal large(≥ 3 cm) white-matter hypointense lesions in T1 W and hyperintense in T2 W and fluid-attenuated inversion recovery images and patchy enhancement. A diagnosis of tumefactive MS was given. She received two consecutive 5-d courses of 1 g daily intravenous methylprednisolone for 2 mo and oral prednisolone in dose of 80 mg twice/daily in between. At the 3rd month, Mini Mental State Examination and VEPs returned to normal but not the MRI. Patient continued oral steroids after hospital discharge(March 2010) for 9 mo with significant MRI improvement after which tapering of steroids started for a year. The patient refused immunomodulation therapy due to her low socioeconomic status. Neither clinical relapse nor new MRI lesions were observed throughout the next 4 years. In spite of the aggressive course of tumefactive MS variant, good prognosis may be seen in some patients.