基于LBP的改进Random Walks算法在颅脑磁共振影像分割中的应用

目的 由于颅脑结构复杂且颅脑磁共振影像易受噪声、磁场不均匀性、部分容积效应等因素的影响,精确的脑组织分割方法仍需深入研究.方法 本文提出一种基于Random Walks的改进算法以提高脑白质、脑灰质及脑脊液分割的准确性.通过引入局部二值模式(local binary patterns,LBP)改进了传统Random Walks权重函数的构造,在反映相邻像素灰度变化信息的同时包含了局部图像的纹理信息,有利于合并同质区域并增强边缘轮廓的识别.本文还使用了灰度先验概率模型减少Random Walks种子点交互的次数.结果 实验结果表明基于LBP的改进算法在多种不同水平的噪声及不均匀场作用下,能够有效识别磁共振影像中脑组织区域的边缘轮廓,并对噪声有良好的鲁棒性.结论 基于LBP的改进Random Walks算法可精确分割颅脑磁共振影像.     

基于非局域注意力和多任务学习的胸部X线片肺实质分割方法

肺实质的准确分割是计算机辅助影像学诊断肺部疾病的关键。随着深度学习技术的发展，基于全卷积网络的图像分割模型取得了很好的效果，但对于边缘模糊和肺实质密度不均匀的情形仍会误分割。针对该问题，本文提出一种基于非局域注意力机制和多任务学习的胸部X线片图像肺实质分割方法。首先，基于残差连接的编-解码卷积网络提取肺实质多层级语义特征信息并预测肺实质边界轮廓；其次，通过非局域注意力机制建立肺实质轮廓与全局语义特征信息之间的相关性并增强轮廓区域特征信息权重；再次，基于增强的特征信息进行多任务监督学习，实现肺实质的准确分割；最后，在JSRT和Montgomery公开数据集上验证了本文方法的有效性和模型泛化能力，对比其他几种代表性的分割模型，其Dice系数和准确性最大分别提高1.99%和2.27%。实验结果表明，通过增强特征信息中边界轮廓的注意力，能有效减少肺实质密度不均匀时的误分割并提高模糊边缘的分割精度。     

基于改进的CV-RSF模型的甲状腺结节超声图像自适应分割算法

目的甲状腺结节超声图像的精确分割对甲状腺结节的良恶性诊断尤为重要。目前,对于甲状腺结节超声图像的分割,有学者提出利用主动轮廓模型分割算法,但是由于活动轮廓分割算法需要手动设置迭代次数,未实现模型的自适应性。因此,本文提出了一种基于改进的无边缘主动轮廓-局部区域可控的拟合(Chan-Vese-region scalable fitting,CV-RSF)模型的甲状腺结节超声图像自适应分割算法。方法选取南京同仁医院12例患者的甲状腺结节超声图像用于实验。首先,在无边缘主动轮廓(Chan-Vese,CV)模型中,引入一个基于梯度的边缘引导函数,根据面积变化率,自适应地获取甲状腺结节的粗分割轮廓;然后,将粗分割轮廓作为局部区域可控的拟合(region-scalable fitting,RSF)模型的初始轮廓,并根据面积变化率,自适应地获取甲状腺结节最终分割结果。将改进模型分割的结果与CV模型、RSF模型分割的结果进行比较,并分析甲状腺结节边缘清晰度对分割结果的影响。结果本文模型算法分割结果的平均迭代次数、平均面积重叠率、平均Hausdorff分别达到了134、90.34%、9.77,均优于CV模型、RSF模型的分割算法。结论该算法有效地分割出边缘清晰和不清晰的甲状腺结节超声图像,并解决手动设置迭代次数的问题,从而实现甲状腺结节的有效、准确、自动分割。     

基于二进小波变换和快速主动轮廓模型的医学图像边缘提取

医学图像的病灶边缘一般呈弱边缘特性,噪声干扰使得提取病灶边缘更加困难,传统的分割方法不能取得令人满意的效果.我们提出了一种基于二进小波变换和主动轮廓模型的病灶边缘提取方法.该方法采用二进小波检测出真正的边缘点,将其作为初始轮廓,再利用改进的快速主动轮廓模型算法连接边缘点,得到病灶的边缘.将该算法用于脑部MRI的肿瘤边缘提取的实验结果表明这种方法可以有效减少噪声的影响,能够准确地提取出复杂的病灶边缘.     

基于测地线活动轮廓模型的图像联合分割算法

为了辅助医生对肿瘤治疗方案和靶区形状的设计,我们研究了PET/CT图像联合自动分割,将计算机自动分割的结果作为一个较客观的依据。传统的测地线活动轮廓模型(GAC)具有边缘演化迅速,对弱边界也能准确分割的优点,但是该算法只能利用一种模态的图像信息进行分割。本研究算法在传统的测地线活动轮廓模型基础上进行改进,重新设计其边缘函数,综合利用了CT信息与PET信息,使算法利用两种模态的医学图像信息进行联合分割。由于边缘函数中结合了两种信息,所以算法的演化收敛速度有一定的提升,分割出的边缘也更加合理,较单一PET图像分割算法具有更准确的边界。     

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。     

基于对称区域生长和边缘梯度的视神经纤维的分割

在视神经横切面图像中,将每个神经纤维的内外边界进行精确分割是视神经形态分析的重要环节,提出一种基于对称区域生长和髓鞘边缘梯度的有效分割算法.该算法分两步进行,首先根据交互方式下选取的种子点,由对称区域生长算法实现轴突分割,然后在轴突轮廓模型基础上,髓鞘外轮廓在髓鞘平均边缘梯度引导下进行演化,实现自动分割.与K-均值聚类,局部阈值和水平集等其他算法的实验结果相对照显示,该算法分割获得的轴突和髓鞘轮廓与实际轮廓相吻合,其分割结果可以作为后续神经纤维形态分析的基础.     

基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘

提出一种基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘的算法.该算法在Chan-Vese (CV)模型基础上,定义了一个局部调整项,采用基于水平集的动态轮廓模型提取超声图像乳腺肿瘤边缘.将该算法应用于89例临床超声图像乳腺肿瘤的边缘提取实验,结果表明:该算法比CV模型更适用于具有区域非同质性的超声图像的分割,可有效实现超声图像乳腺肿瘤边缘的提取.     

基于全局和区域可伸缩拟合局部熵活动轮廓模型的超声图像分割

针对区域可伸缩拟合局部熵(region-scalable fitting based on local entropy,RSF_LE)模型图像分割效率低的问题,本研究提出一种改进的RSF_LE模型。定义带有加权局部灰度拟合项以及辅助的加权全局灰度拟合项的能量泛函,其中加权局部灰度拟合项负责对目标边界附近的轮廓进行诱导,使其靠近目标物边界,加权全局灰度拟合项利用图像的全局信息来引导远离目标的轮廓向目标靠拢,该方法可以克服传统的RSF_LE模型分割算法效率低下的问题,并提高了该方法的鲁棒性。     

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。     

基于贪婪算法的Snake模型

目的：为改善经典活动轮廓模型的缺陷。方法：本文提出一种新的基于贪婪算法的活动轮廓模型,对其内部能量中加入轮廓平均长度项的控制,外部能量中加入梯度方向势能,并提出区域能量在贪婪算法中的快速求解方法。另外采用动态调整蛇点的算法,使蛇点数目能够自适应地变化。结果：通过与传统的GVF算法分割结果比较,本文的分割效果较理想。结论：说明该方法分割准确性高并且对于用户的初始轮廓选择要求不高,具有一定的实用价值。     

Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour

Knowing the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy, a commonly used prostate cancer treatment method. The prostate boundary must be segmented before a dose plan can be obtained. However, manual segmentation is arduous and time consuming. This paper introduces a semi-automatic segmentation algorithm based on the dyadic wavelet transform (DWT) and the discrete dynamic contour (DDC). A spline interpolation method is used to determine the initial contour based on four user-defined initial points. The DDC model then refines the initial contour based on the approximate coefficients and the wavelet coefficients generated using the DWT. The DDC model is executed under two settings. The coefficients used in these two settings are derived using smoothing functions with different sizes. A selection rule is used to choose the best contour based on the contours produced in these two settings. The accuracy of the final contour produced by the proposed algorithm is evaluated by comparing it with the manual contour outlined by an expert observer. A total of 114 2D TRUS images taken for six different patients scheduled for brachytherapy were segmented using the proposed algorithm. The average difference between the contour segmented using the proposed algorithm and the manually outlined contour is less than 3 pixels.     

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.     

Multiscale active contour model for vessel segmentation

This paper presents a novel multiscale active contour model for vessel segmentation. The model is based on accurate analysis of the vessel structure in the image. According to different scale response of the eigenvalues of local second order derivative (Hessian matrix), a new vessel region information function, which shows a valid estimation of the vesselness measure, is defined. We introduce the posteriori probability estimation into the active contours framework and design a new objective function. The defined objective function is minimized using the variational method, and a new region-based external force is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. This active contour model combines the obtained region-based and conventional boundary-based force, which aims at finding more accurate vessel edges even when the vessel branches are low contrast or blurry. Furthermore, the proposed model is implemented by an implicit method of level set framework, the solution of which is steady and suitable for various topology changes. Moreover, two new speed functions for vessel segmentation in the level set method are presented, one for fast marching and the other for a narrow-band algorithm. The vessel segmentation experiments compared with previous geometric active contour models are shown on several medical images. The experimental results demonstrate the performance of our approach.     

基于形变模型的医学图像分割算法研究

结合形变模型和模糊C-均值（FCM）分割技术，提出了一种基于形变模型的医学图像解剖结构轮廓分割方法，在FCM分类的基础上，利用成员隶属函数定义一种模糊约束力并附加于形变模型的外部约束力中．在该种复合外部约束作用下，使形变模型能更好地收缩于解剖结构的轮廓。图像实验结果表明该方法的有效性和可行性。     

一种基于改进的DDCM可形变模型的医学图像分割算法

提出一种基于传统DDCM模型的具有自适应能力的可形变模型算法。利用局部归一化Snakes外力场的方法,提高模型的抗噪声能力,同时降低对初始位置的敏感性;构造图像边界信息图,用统计学方法获得Snakes的即时位置信息,解决传统模型的难于收敛到凹型边界、收敛条件不易控制以及移动步长的单一等问题。这种改进的可形变模型具有一定的自适应能力和较强的鲁棒性,而且效率高,在临床中有一定的实用价值。     

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.     

A dual-stage method for lesion segmentation on digital mammograms

Mass lesion segmentation on mammograms is a challenging task since mass lesions are usually embedded and hidden in varying densities of parenchymal tissue structures. In this article, we present a method for automatic delineation of lesion boundaries on digital mammograms. This method utilizes a geometric active contour model that minimizes an energy function based on the homogeneities inside and outside of the evolving contour. Prior to the application of the active contour model, a radial gradient index (RGI)-based segmentation method is applied to yield an initial contour closer to the lesion boundary location in a computationally efficient manner. Based on the initial segmentation, an automatic background estimation method is applied to identify the effective circumstance of the lesion, and a dynamic stopping criterion is implemented to terminate the contour evolution when it reaches the lesion boundary. By using a full-field digital mammography database with 739 images, we quantitatively compare the proposed algorithm with a conventional region-growing method and an RGI-based algorithm by use of the area overlap ratio between computer segmentation and manual segmentation by an expert radiologist. At an overlap threshold of 0.4, 85% of the images are correctly segmented with the proposed method, while only 69% and 73% of the images are correctly delineated by our previous developed region-growing and RGI methods, respectively. This resulting improvement in segmentation is statistically significant.     

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。     

用改进的Snake算法检测血管内外轮廓

动态轮廓模型 (Snake算法 )是一种较好的目标轮廓检测方法。但是传统的Snake算法在对其能量函数进行优化时 ,不能检测多目标图像 ,并且不能检测凹形目标和内轮廓。本文首先对图像进行自动Snake初始化 ,然后在Snake的成长过程中加入向心力因子。实验结果表明 ,新的算法能够检测多目标轮廓和凹形目标及内轮廓 ,优于传统的Snake算法。