基于改进模糊连接度的CT图像肝脏血管三维分割方法

解决传统模糊连接度难以较好分割CT图像肝血管、需要多个种子点和较耗时等问题。改进传统模糊连接度分割算法:对最新的Jerman血管增强算法进行改进;将改进的血管增强响应引入模糊亲和度函数;使用Otsu多阈值算法代替置信连接度,进行模糊连接度算法的初始化。预处理包括自适应S型非线性灰度映射和各向同性插值采样;随后,执行改进的Jerman血管增强算法;再将其增强响应引入模糊亲和度函数,同时利用Otsu多阈值算法统计前景目标信息,对模糊连接度进行初始化;最终,结合单一种子点实现三维肝脏血管的自动分割。选用内含20例CT的公开数据集,定量评估改进的血管增强算法和模糊连接度分割算法。评价标准主要包括对比度噪声比、准确性、敏感性和特异性。该血管增强算法的平均对比度噪声比为8.43 dB,优于传统血管增强算法。该血管分割算法的准确性达98.11%,优于基于置信连接度的传统模糊连接度分割算法、区域生长算法和水平集分割算法。此外,在分割算法的耗时方面,该算法也具有明显优势。提出的三维分割方法能有效解决传统模糊连接度分割CT影像中肝血管结构的不足,可提升分割精度和效率。     

基于模糊连接度的多发性硬化症MR图像自动分割算法

多发性硬化症(MS)是一种严重威胁中枢神经功能的疾病,利用磁共振成像技术能够无损伤地检出其病灶。为了自动地对多发性硬化症病灶进行分割,提出了基于模糊连接度的分割算法,实现了种子点的自动选取。作为多发性硬化症分割的预处理,针对脑部MR FLAIR图像的特征,基于区域增长方法,还提出了脑部组织提取算法。通过对临床患者MR图像的分割实验,表明该分割算法能够比较准确地分割多发性硬化症病灶,其分割效果明显好于模糊C-均值聚类算法和基于马尔可夫场模型的分割算法。该算法还具有无监督、运算速度快、稳健性好等优点,能够应用于多发性硬化症的临床辅助诊断。     

基于相对模糊连接度的联合主动轮廓模型及其在医学图像分割中的应用

针对医学图像背景复杂、边界模糊、局部不均匀等特点,提出了一种基于相对模糊连接度的联合主动轮廓模型,并将其应用于医学图像分割。首先介绍主动轮廓模型的曲线演化方程和模糊连接度的相关理论,然后将相对模糊连接度作为曲线演化驱动力引入曲线演化方程,最后用实验证明该方法对多目标医学图像和复杂医学图像的有效性。由于模糊连接度方法综合了局部信息和全局信息,因此可以克服Li方法容易陷入局部最优的问题和Chan-Vese方法不能越过局部伪边界的问题,从而使联合主动轮廓模型的演化曲线最终收敛于全局最优边界。     

神经核团异常放电的数值模拟与FPGA实现

进行神经核团网络异常放电的数值模拟以及硬件实现。方法:选用Izhikevich模型模拟单个神经元放电,通过化学突触连接各神经元搭建神经核团网络,进而在FPGA上实现神经核团放电。对比Izhikevich模型在Simulink和DSP Builder两种软件中建模的相对标准误差,并分析各神经核团的放电特性。结果:两种软件中各神经核团的仿真结果相对标准误差均小于0.1,验证了建模的一致性。通过计算丘脑的中继可靠性指标RI=0.3<0.6,证明搭建的神经核团网络可以模拟帕金森病的一种放电状态。结论:模拟神经核团的异常放电对替代动物活体实验、探究神经性疾病的治疗方法和脑机接口研究等具有重要意义。     

用于MRI脑组织分割的自动模糊连接方法

本研究提出了一种自动化的模糊连接（fuzzy connectedness，FC）方法，用于3维核磁共振（MRI）图像脑组织分割。方法的主要创新在于提出了FC方法中各项参数的自动指定方法，包括：利用灰质、白质各自的体素尺度（scale）值大小差异，自动估计组织的灰度概率密度函数；根据估计得到的组织灰度概率密度函数，自动指定种子点。从而避免了人工干预，保证了分割过程的自动化和可重复性。所提方法在IBSR（the Internet Brain Segmentation Repository）数据库所提供的MRI图像上进行了测试，并和同类研究进行了对比，分割精度优于同类研究。作为一种完全自动化的方法，该方法能够被广泛应用到3维可视化、放疗手术计划和医学数据库构造中。     

尾状核、豆状核和丘脑的三维可视化及与磁共振图像的融合

目的依据中国虚拟人数据集对尾状核、豆状核和丘脑进行三维重建并与临床影像数据进行融合，为神经外科疾病的诊断与治疗提供形态学依据。方法以中国虚拟人数据集为依据，采用图像透明的方法对尾状核、豆状核和丘脑进行手工分割，在平滑处理中运用了腐蚀与膨胀的方法，并把分割后的神经结构与病人磁共振图像进行融合，最后用表面绘制与体绘制相结合的方法进行三维重建。结果重建后的图像可清晰显示尾状核、豆状核、丘脑各个神经结构的形态、位置和毗邻关系。可在三维空间中绕任意轴旋转任意角度，从不同的方向进行观察。融合后的图像能同时显示虚拟结构与病人MRI图像。结论用三维可视化与MRI图像融合方法获得的图像资料，对于解剖学的教学研究以及神经外科疾病的诊断与治疗具有重要的参考价值。     

基于区域生长法的三维心血管模型的分割

目的医学图像能够反映人体各组织的生理或病理性的结构信息和功能信息,根据图像序列,对患者心血管组织进行三维重建,可以方便医生对血管的病灶部位做出准确诊断。本文将区域生长法(region growing method,RGM)在二维图像的基础上用于三维,并比较RGM的3种子方法对心血管模型进行三维分割的效果。方法结合VTK(Visualization Toolkit)与ITK(Insight Segmentation and Registration Toolkit)函式库,首先利用光线投射法(ray casting,RC)对患者的CT扫描图像序列进行三维重建,得到患者胸腔三维模型;之后分别利用区域生长法中连通阈值、置信连接和邻域连接3种算法进行三维分割处理,得到患者心血管模型的仿真结果。结果三维重建仿真技术,具有直观重现心血管造影序列所描述的人体胸腔部位各组织的可能性;3种分割算法的比较显示,邻域连接法相比于其他两种算法,模型的解剖结构信息丢失程度较大,血管树的分支数目及管径也明显偏小。结论使用VTK与ITK函式库,通过光线投射法的体绘制技术和区域生长法的图像分割技术,可以建立患者的心血管模型;区域生长法中的连通阈值和置信连接算法,对结果的细节保留程度最大。     

基于SMDC连接代价算子的肺血管分割算法研究

针对CT肺图像中的血管分割,提出一种基于SMDC连接代价算子的分割方法。首先设计一种背景填充技术,对灰度变换范围有选择性地收缩或拉伸,有效地消除背景噪声对血管分割的影响,然后应用SMDC连接代价算子对肺部区域进行分割,最后用实验证明了该方法的有效性。相似度分析表明自动与手工分割结果吻合率高于95%,并能较好地保留细节。     

一种脑MRA图像自动分割算法及其在图像导引神经外科中的应用

图像导引神经外科技术是在神经外科手术过程中减小患者刨伤的有效方法。在这种方法中，医学图像分割质量的好坏直接影响着手术过程的准确性。本研究提出了一种新的基于MRA图像的自动分割算法，这种算法通过各向异性滤波，统计阈值分割，数学形态学滤波，和基于边界距离场的活动轮廓模型来对MRA图像进行自动分割和分割结果的可视化。数据实验表明用这种算法对MRA图像进行分割的结果可以有效地用于图像导引神经外科，并且算法具有一定的鲁棒性。     

基于改进支持向量机算法的超声图像分割技术

超声图像的边缘分割受到噪声影响,基于传统支持向量机(support vector machine,SVM)超声图像分割过程存在较大缺陷。提出一种基于改进SVM算法超声图像分割算法。采用分区域特征匹配方法,进行二维超声图像的分块融合性检测和特征块匹配,根据超声纹理的规则性特征分量进行病理边缘特征提取,利用提取的精度作为约束条件,优化SVM分割过程,进行超声图像分割过程的自适应分类,实现对超声图像的快速分割。仿真结果表明,采用该方法进行超声图像分割的精度较高,对超声图像的病理特征识别能力较好,结构相似度信息较强,提高了超声图像检测和诊断分析能力。     

Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images

Uncontrollable and unlimited cell growth leads to tumor genesis in the brain. If brain tumors are not diagnosed early and cured properly, they could cause permanent brain damage or even death to patients. As in all methods of treatments, any information about tumor position and size is important for successful treatment; hence, finding an accurate and a fully automated method to give information to physicians is necessary.A fully automatic and accurate method for tumor region detection and segmentation in brain magnetic resonance (MR) images is suggested. The presented approach is an improved fuzzy connectedness (FC) algorithm based on a scale in which the seed point is selected automatically. This algorithm is independent of the tumor type in terms of its pixels intensity. Tumor segmentation evaluation results based on similarity criteria (similarity index (SI), overlap fraction (OF), and extra fraction (EF) are 92.89%, 91.75%, and 3.95%, respectively) indicate a higher performance of the proposed approach compared to the conventional methods, especially in MR images, in tumor regions with low contrast. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, chemotherapy, and radiotherapy procedures.     

Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.     

Segmentation of nerve fibers using multi-level gradient watershed and fuzzy systems

ObjectiveThis paper presents an algorithm based on multi-level watershed segmentation combined with three fuzzy systems to segment a large number of myelinated nerve fibers in microscope images. The method can estimate various geometrical parameters of myelinated nerve fibers in peripheral nerves. It is expected to be a promising tool for the quantitative assessment of myelinated nerve fibers in related research.Materials and methodsA novel multi-level watershed scheme iteratively detects pre-candidate nerve fibers. At each immersion level, watershed segmentation extracts the initial axon locations and obtains meaningful myelinated nerve fiber features. Thereafter, according to a priori characteristics of the myelinated nerve fibers, fuzzy rules reject unlikely pre-candidates and collect a set of candidates. Initial candidate boundaries are then refined by a fuzzy active contour model, which flexibly deforms contours according to the observed features of each nerve fiber. A final scan with a different set of fuzzy rules based on the a priori properties of the myelinated nerve fibers removes false detections. A particle swarm optimization method is employed to efficiently train the large number of parameters in the proposed fuzzy systems.ResultsThe proposed method can automatically segment the transverse cross-sections of nerve fibers obtained from optical microscope images. Although the microscope image is usually noisy with weak or variable levels of contrast, the proposed system can handle images with a large number of myelinated nerve fibers and achieve a high fiber detection ratio. As compared to manual segmentation by experts, the proposed system achieved an average accuracy of 91% across different data sets.ConclusionWe developed an image segmentation system that automatically handles myelinated nerve fibers in microscope images. Experimental results showed the efficacy of this system and its superiority to other nerve fiber segmentation approaches. Moreover, the proposed method can be extended to other applications of automatic segmentation of microscopic images.     

基于VIP-Man图谱配准的腹部器官分割方法

提出了一种基于图谱配准的腹部器官分割方法.首先将一套预标记图谱向个体图像进行配准,建立二者之间器官的基本对应关系,同时完成对感兴趣器官的识别,其中配准包含全局配准和器官配准.然后,借助已配准的图谱,采用模糊连接方法对感兴趣器官进行分割.腹PCT和MR实验测试结果证明:这种方法实现了模糊连接分割方法中各项参数的自动指定,减轻了人工负担,提高了结果的可靠性.     

Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI

Precise segmentation of stroke lesions from brain magnetic resonance (MR) images poses a challenging task in automated diagnosis. In this paper, we proposed a new method called watershed-based lesion segmentation algorithm (WLSA), which is a novel intensity-based segmentation technique used to delineate infarct lesion in diffusion-weighted imaging (DWI) MR images of the brain. The algorithm was tested on a series of 142 real-time images collected from different stroke patients reported at IMS and SUM Hospital. One MRI slice having largest area of infract lesion is selected from each patient from multiple slices. The main objective is to combine the strength of guided filter and watershed transform through relative fuzzy connectedness (RFC) to detect lesion boundaries appropriately. The extracted informative statistical and geometrical features are used to classify the types of stroke lesions according to the Oxfordshire Community Stroke Project (OCSP) classification. The experimental results demonstrated the effectiveness of the proposed process with high accuracy in delineating lesions. A classification with a dice similarity index (DSI) of 96% with computational time of 0.06 s in random forest (RF) and an accuracy of 85% with computational time of 0.84 s has been obtained by multilayer perceptron (MLP) neural network classifier in tenfold cross-validation process. Better detection accuracy is achieved in RF classifier in classifying stroke lesions.     

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 +/- 0.51 pixels (0.54 +/- 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.     

基于改进图割的超声瞳孔图像分割与直径测量

传统的瞳孔直径测量是通过医生手工标定,对于眼外伤和丧失意识的患者测量不方便。针对瞳孔直径测量的人工交互量大且测量鲁棒性不强的问题,采用图割算法分割瞳孔超声图像并测量瞳孔直径。对传统图割算法进行两个方面的改进,采用自适应阈值的区域生长代替人为种子点选取,在保证分割效果的基础上减少了图割的交互量;在能量函数的数据项部分增加图像的梯度信息,减少了原始算法分割结果中出现的小区域,增强了对弱边缘的分割。最后,对采集到的超声瞳孔图像进行自动分割、自动测量瞳孔直径,可以得到患者瞳孔的直径动态变化,给临床诊断提供依据。为了验证算法的有效性,对10位患者的动态瞳孔超声图像进行基于改进图割的瞳孔直径测量,并与医生的手动测量结果对比。结果表明,本方法的结果与医生手动测量结果的绝对误差小于0.2 mm,相关系数不小于0.83。通过改进图割算法,改善了分割效果,实现了超声瞳孔动态图像的自动直径测量,并可有效代替瞳孔直径的人工测量,减少人工交互量。