股骨CT图像的边缘提取

为重建和测量股骨的解剖结构,需要大量地读取CT图像的信息,以获得股骨轮廓的坐标值.本研究采用直方图阈值图像分割、Kirsh边缘提取方法获得股骨的二值化轮廓图像.轮廓的提取应用了"迷宫"边缘跟踪算法.本方法可大量、快捷、正确地提取图像轮廓信息.     

股骨CT图像轮廓跟踪方法

目的 为重建和测量股骨的解剖结构，需要大量地读取CT图像的信息，以获得股骨轮廓的坐标值。方法本研究采用直方图阈值图像分割、Kirsh边缘提取法获得股骨的二值化轮廓图像。结果轮廓坐标的提取应用了“迷宫”边缘跟踪算法。结论本方法可大量、快捷、正确地提取图像轮廓信息。     

基于Contourlet变换和PCNN的CT图像椎体解剖轮廓特征提取方法的研究

提出一种新的基于Contourlet变换和脉冲耦合神经网络(PCNN)的医学图像解剖轮廓特征提取算法。首先对原始椎体CT图像进行Contourlet变换,得到能稀疏表示图像边缘以及方向信息的子带和低频子带;然后结合PCNN对低频子带进行边缘轮廓细节提取,最后利用处理后的所有子带系数,通过Contourlet逆变换,提取出图像的边缘轮廓。实验将本算法提取的结果与Canny算子、区域生长法以及结合小波变换和PCNN的算法提取的图像边缘轮廓进行比较,结果表明新算法能够有效的实现医学图像解剖结构轮廓特征的提取。     

MRI图像中颅骨不连续外轮廓的多分辨率提取

目前,许多医学图像处理（例如图像三维重构,图像建模,图像配准和图像融合等）都需要进行图像的边缘提取,因此,边缘提取方法在医学图像处理中有极其重要的意义,轮廓闭合图像的边缘比较容易提取,而解剖和外伤等原因造成轮廓不连续时边缘提取比较复杂,美国和欧洲等发达国家从20世纪90年代后期开始着手医学图像不连续边缘提取方法的研究,国内的有关研究仍少见报道,本课题以脑图像为例,试图综合运用多分辨率法与八邻距离转换法来提取脑图像的不连续外轮廓,研究结果表明,此方法在提取不连续脑图像的连缘时快速有效,同时可以去掉高频干扰,有利于图像的配准。     

一种基于多尺度分析的CT图像边缘检测方法

为了准确提取CT图像中解剖组织几何形态特征,提出了一种基于多尺度分析的CT图像边缘检测方法。本文应用多尺度分析中含有尺度因子的平滑函数的负导数作为小波,对CT图像实施小波变换,并检测小波变换的模局部极大值,完成基于模局部极大值的解剖组织轮廓特征表达。本文还讨论了一种模局部极大值点的简单筛选方法,针对CT图像噪声较大的特点,以模局部极大值的均方根乘以一个与尺度有关的因子作为模局部极大值的阈值,在不同尺度上获得了清晰的边缘信息。阈值处理后的模局部极大值图表明,不同尺度下的边缘检测能给出大小不同的物体的边缘信息。本方法能在有效抑制噪声的基础上,准确提取感兴趣解剖组织的几何轮廓特征。     

基于活动轮廓模型和边缘对比度特征量的血管内超声图像边缘提取

血管内超声（IVUS）图像冠状动脉血管壁的边缘提取对冠状动脉疾病的诊断和治疗有着重要意义。本研究提出了一种用于自动提取IVUS序列图像冠状动脉血管壁内、外膜边缘的方法。该方法基于活动轮廓模型以及本研究所定义的边缘对比度特征量，利用Hopfield神经网络并结合模拟退火算法自动提取IVUS序列图像冠状动脉血管壁的内、外膜边缘。实验结果表明，本研究方法易于实现，而且准确性和可靠性较高，对IVUS序列图像处理的可重复性和鲁棒性较好，是一种较好的全局最优化算法。     

针对颅脑CT的边缘提取

边缘提取是ＣＴ图像三维重建前期工作中的关键步骤。本文根据图像的统计信息，一方面利用类间方差测度准则，求取图像的灰度阈值；另一方面求取图像的梯度阈值，利用这两个阈值将颅脑ＣＴ图像中各器官的边缘提取出来，作为三维重建的轮廓输入。实验结果表明：这种方法实时性好，适应性强，提取的边缘清晰完整，准确性高，证明了该算法的实用性和可行性     

基于混合水平集的脑胶质瘤分割方法

全面考虑脑胶质瘤分割图像的边界信息和区域信息,在水平集的基础上,将基于边缘检测的活动轮廓模型(GAC模型)和局部图像拟合模型(LIF模型)相结合,提出一种混合水平集的分割方法。首先,对脑胶质瘤MR图像进行预处理,采用C-V模型提取脑组织;然后,创建混合水平集模型,对脑组织图像中的脑胶质瘤进行分割。实验证明,本研究的分割方法可以简化水平集符号距离函数的正则化过程,并且可有效克服GAC模型在弱边缘或离散边缘处产生的边界泄漏的问题,从而取得较好的分割结果。     

肝脏动、静脉相CT图像配准与融合方法研究

目的 研究肝脏CT扫描序列图像轮廓提取、配准与融合问题.方法 采用图像滤波去噪、增强图像边缘及提取图像外轮廓等方法对CT序列图像进行预处理.对肝脏CT扫描序列图像动脉相位期与静脉相位期的图像轮廓进行配准,选取最优配准参数确定不同相位期图像的对应关系,以实现配准.将配准后对应的动、静脉相位期图像融合.结果 融合后的图像展现了同一位置不同相位期肝脏动、静脉的情况.结论 配准、融合后的图像能提供更加丰富的信息,可为医生临床诊断提供参考.     

一种基于图像分割的医学图像融合方法

提出一种新的图像融合算法 ,用于临床治疗计划设计时对病灶的确定。文中采用改进的Canny算子对病灶边缘提取方法进行了研究 ,根据局部直方图计算对非目标轮廓进行抑制。通过对图像配准建立空间映射关系 ,将一种图像中的病灶边缘特征与其它相应的图像进行叠加 ,获得具有病灶边缘和解剖结构特征的融合图像。本文的融合算法简单、直观 ,临床实用性强     

结合区域信息的分段活动轮廓模型

提出了一种结合区域信息的分段活动轮廓模型，利用边缘信息迅速找到对象的大体轮廓，然后结合区域统计信息使模型精确收敛到对象边缘。分段的层次化变形有效的利用了图像的全局和局部信息，使用仿射变换使模型的局部以同一种变换方式变形，提高模型对噪声和伪边缘的鲁棒性，同时保持模型轮廓形状的一致性。在精确匹配阶段利用区域统计信息重新定义模型的外部能量，采用自适应的搜索区域确定方法，提高了算法的效率和进入凹边缘的能力。试验表明本模型运算速度快，抗噪声和避免陷入局部极小值的能力较强，有较好的分割效果。     

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.     

Segmentation of the prostate from suprapubic ultrasound images

We present a technique for semiautomated segmentation of human prostates using suprapubic ultrasound (US) images. In this approach, a speckle reducing anisotropic diffusion (SRAD) is applied to enhance the images and the instantaneous coefficient of variation (ICOV) is utilized for edge detection. Segmentation is accomplished via a parametric active contour model in a polar coordinate system that is tailored to the application. The algorithm initially approximates the prostate boundary in two stages. First a primary contour is detected using an elliptical model, followed by a primary contour optimization using an area-weighted mean-difference binary flow geometric snake model. The algorithm was assessed by comparing the computer-derived contours with contours produced manually by three sonographers. The proposed method has application in radiation therapy planning and delivery, as well as in automated volume measurements for ultrasonic diagnosis. The average root mean square discrepancy between computed and manual outlines is less than the inter-observer variability. Furthermore, 76% of the computer-outlined contour is less than 1 sigma manual outline variance away from "true" boundary of prostate. We conclude that the methods developed herein possess acceptable agreement with manually contoured prostate boundaries and that they are potentially valuable tools for radiotherapy treatment planning and verification.     

Identification of the breast boundary in mammograms using active contour models

A method for the identification of the breast boundary in mammograms is presented. The method can be used in the preprocessing stage of a system for computeraided diagnosis (CAD) of breast cancer and also in the reduction of image file size in picture archiving and communication system applications. The method started with modification of the contrast of the original image. A binarisation procedure was then applied to the image, and the chain-code algorithm was used to find an approximate breast contour. Finally, the identification of the true breast boundary was performed by using the approximate contour as the input to an active contour model algorithm specially tailored for this purpose. After demarcation of the breast boundary, all artifacts outside the breast region were eliminated. The method was applied to 84 medio-lateral oblique mammograms from the Mini-MIAS database. Evaluation of the detected breast boundary was performed based upon the percentage of false-positive and false-negative pixels determined by a quantitative comparison between the contours identified by a radiologist and those identified by the proposed method. The average false positive and false negative rates were 0.41% and 0.58%, respectively. The two radiologists who evaluated the results considered the segmentation results to be acceptable for CAD purposes.     

Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model

In this paper, we propose a novel approach to cell image segmentation under severe noise conditions by combining kernel-based dynamic clustering and a genetic algorithm. Our method incorporates a priori knowledge about cell shape. That is, an elliptical cell contour model is introduced to describe the boundary of the cell. Our method consists of the following components: (1) obtain the gradient image; (2) use the gradient image to obtain points which possibly belong to cell boundaries; (3) adjust the parameters of the elliptical cell boundary model to match the cell contour using a genetic algorithm. The method is tested on images of noisy human thyroid and small intestine cells.     

一种基于Snake模型的脑部CT图像分割新算法

针对目前传统的Snake模型图像分割算法的力场捕捉范围小、对初始轮廓的选取敏感以及对轮廓曲线难以收敛到 细小深凹边界的缺陷,提出一种基于Snake 模型的脑部CT图像分割新算法。算法首先运用Canny 边缘算子对图像进行 边缘检测,将边缘检测图像叠加到原始图像上,然后再运用Snake模型和梯度向量流（GVF）Snake模型分别对叠加图像进 行分割。实验结果表明,该算法克服了传统Snake 模型和GVF Snake 模型因边缘轮廓不清晰造成的漏分割情况,防止了 GVF Snake模型由于GVF力场的相互作用所造成的过分割现象,同时,还能促使轮廓线收敛到细小深凹边界,提高定位精 度,具有更好的分割效果。     

基于单水平集函数的细胞分割算法

目的：由于细胞图像十分复杂,传统的基于像素或者边界的图像分割方法难以精确的实现细胞分割。因此,需要设计一种可以实现细胞图像精确分割的方法。方法：结合大津分割算法和主动轮廓模型的优点,设计出一种基于单水平集函数的细胞分割算法,首先对细胞图像大津分割,其结果作为水平集函数的初始值,然后使用迭代法对水平集函数演化。采用MATLAB对显微镜下获取的细胞图像进行试验,将本文改进后的算法与常规的算法进行了对比。结果：与传统的水平集分割算法相比,本文方法对细胞图像分割结果更加准确,迭代次数减少一半左右,因此分割时间也减少了一半左右。结论：结合细胞图像的结构特点,利用大津分割结果作为主动轮廓模型的初始值,可有效解决主动轮廓模型因为初始值设置不当导致的分割缺陷问题,水平集函数能够跟踪拓扑结构变化,具有计算精度高、算法稳定、优化边界清晰光滑等优点,在本文中得到了充分的应用。因此本文所提出的算法能够高效地实现细胞图像的分割。     

Outlining the prostate boundary using the harmonics method

In a computerised ultrasound image guidance for automated prostatectomy system, it is necessary to identify a smooth, continuous contour for the prostate (boundary) from the ultrasound image. The radial bas-relief (RBR) method, which has been reported previously, can extract a skeletonised image from an ultrasound image automatically. After this process the prostate boundary is clearly revealed. However, analysis of the image is far from complete, as there are many spurious branches that create too much ambiguity for the system to define the actual boundary. There are also sections missing from the prostate boundary. Therefore further post-processing is required to describe and define the prostate boundary. In the paper, the harmonics method is used to describe the prostate boundary. The harmonics method uses Fourier information for noise removal and encodes a smooth boundary. The results of using the harmonics method after application of the RBR method on ultrasound images are presented. Factors that affect the performance are also highlighted and discussed.     

一种新的舌癌图像快速自动分割方法

目的:研究一种新的舌癌图像自动分割算法以实现对舌癌肿瘤的快速准确分割。方法:通过引入一种基于局部均方差的自适应尺度算子实现演化曲线在演化过程中的自动调整,从而更高效率地向真实目标边界运动,并且克服舌癌肿瘤图像中目标边界不清和图像灰度不均匀等不良因素带来的影响。此外,为加快曲线的收敛速度,本文提出了一种新的能量项评估演化曲线轮廓内部和轮廓外部区域灰度的分布差异,以此引导曲线自适应地调整演化速度,减少完成分割任务所需的迭代次数。结果:使用本方法对22幅舌癌肿瘤MRI图像进行分割,分割结果与真实结果之间的重叠率Dice值为0.82,豪斯多夫距离HD值为1.732 mm。结论:将本文算法与其它现有的几种活动轮廓模型进行定性和定量对比分析,实验结果表明本文算法在对细节及弱边缘灰度的处理上表现更加优异,可用于舌癌肿瘤的精确分割,为临床分析提供辅助信息。     

Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification

Skull-stripping in magnetic resonance (MR) images is one of the most important preprocessing steps in medical image analysis. We propose a hybrid skull-stripping algorithm based on an adaptive balloon snake (ABS) model. The proposed framework consists of two phases: first, the fuzzy possibilistic c-means (FPCM) is used for pixel clustering, which provides a labeled image associated with a clean and clear brain boundary. At the second stage, a contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of an adaptive balloon snake model. The model is designed to drive the contour in the inward normal direction to capture the brain boundary. The entire volume is segmented from the center slice toward both ends slice by slice. Our ABS algorithm was applied to numerous brain MR image data sets and compared with several state-of-the-art methods. Four similarity metrics were used to evaluate the performance of the proposed technique. Experimental results indicated that our method produced accurate segmentation results with higher conformity scores. The effectiveness of the ABS algorithm makes it a promising and potential tool in a wide variety of skull-stripping applications and studies.