支持向量学习并行区域增长结合活动轮廓模型的图像分割算法

为克服经典区域增长算法门限设置困难和图像分割精度不高的问题,提出了基于支持向量机学习的区域增长与活动轮廓模型结合的高精度图像分割算法。首先交互式选择属于目标区域的子块和背景区域的子块形成支持向量机的训练样本;并利用这些已知的训练样本训练支持向量分类器。在目标与背景的并行竞争增长过程中,利用训练好的支持向量分类器(SVC)进行分类判决,得到目标对象的初始轮廓。为提高分割对象的精度,采用活动轮廓模型获得准确的边缘。仿真实验获得了较好的分割效果,表明该提出的算法是合理可行的。     

Spatio-temporal segmentation of moving objects using edge features in infrared videos

In this paper, a novel method is proposed for spatio-temporal segmentation of moving objects using edge features in infrared videos. We define motion saliency of edge (MSoE) to generate the MSoE-map. The seeds of moving objects are extracted from the MSoE-map by using Otsu's method and subsequently compensated by historical data. An improved layer-based region growing method is applied to the seeds to achieve spatial segmentation of moving objects. The region growing method has an adjustable growing threshold. So, one of the focuses of our work is how to determine the best growing threshold. A Markov Random Field (MRF) based criterion with maximum a posterior (MAP) estimation principle is proposed for performance evaluation of moving object segmentation without ground truth (GT) in infrared videos. This criterion can be considered as an object function of threshold determination during global searching. The global optimum is accomplished by using simulated annealing (SA) algorithm to obtain the best growing threshold. The final segmentation mask of moving objects is grown from the seeds with the best growing threshold. Experimental results are provided to illustrate that the proposed method has better performance for moving object segmentation with fewer effects of object-background misclassification in infrared videos.     

基于高斯混合模型的多区域并行增长图像分割算法

为克服经典区域增长算法中生长规则以及特征选取的困难,提出了基于高斯混合模型的多区域并行区域增长图像分割算法。首先交互选择多个不同区域的种子点,并利用交互式选择的属于每个区域的子块得到混合模型的个数;然后利用最大期望估计混合模型参数作为区域增长的初始参数,并在增长过程中不停地调节模型参数。为了避免初始种子点位置选择对算法性能的影响,采用了多区域并行竞争增长策略。仿真实验获得了较好的分割效果,表明所提出的算法是合理可行的。     

基于区域进化的区域增长图像分割

为克服经典区域增长算法中门限选择困难、分割稳定性不高与串行处理速度慢的不足,提出了基于区域进化的快速区域增长图像分割算法。引入了新的区域能量表示模型,并给出了迭代进化形式。在区域增长过程中,逐渐增加区域增长的门限,通过对能量函数的动态优化来逼近最佳分割结果。仿真实验表明,该方法能有效地避免经典区域增长算法中门限选择的困难,采用区间连通处理技术代替单一像素串行迭代处理方式,可使分割速度提高十多倍。     

Robust protein microarray image segmentation using improved seeded region growing algorithm

Protein microarray technology has recently emerged as a powerful tool for biomedical research. Before automatic analysis the protein microarray images, protein spots in the images must be determined appropriately by spot segmentation algorithm. In this paper, an improved seeded region growing (ISRG) algorithm for protein microarray segmentation is presented, the seeds are obtained by finding the positions of the printed spots, and the protein spot regions are grown through these seeds. The experiment results show that the presented algorithm is accurate for adaptive shape segmentation and robust for protein microarray images contaminated by noise.     

The study and application of the improved region growing algorithm for liver segmentation

In order to improve the accuracy of the medical image segmentation and reduce the effect of selecting seed points using region growing algorithm, an improved region growing method is proposed in this paper. First, the source images are pre-processed using non-linear mapping method and the region of interest in the liver is selected by man–machine interaction; Quasi-Monte Carlo method is used for generating low-dispersion sequences points in the region of interest and the optical seed points are selected by computing these points; In addition, the region growing criteria is also improved. The improved region growing algorithm is used for segmenting three discontinuous abdomen CT images. Compared with the traditional region growing method, the improved method can get better liver segmentation effects. The proposed method can be effectively applied to liver segmentation and it can improve the accuracy of liver segmentation.     

Cellular neural networks for welding arc thermograms segmentation

Machine vision systems are used in many areas for monitoring of technological processes. Among this processes welding takes important place, where often infrared cameras are used. Besides reliable hardware, successful application of vision systems requires suitable software based on proper algorithms. One of most important group of image processing algorithms is connected to image segmentation. Obtainment of exact boundary of an object that changes shape in time, such as the welding arc, represented on a thermogram is not a trivial task. In the paper a segmentation method using supervised approach based on a cellular neural networks is presented. Simulated annealing and genetic algorithm were used for training of the network (template optimization). Comparison of proposed method to a well elaborated segmentation method based on region growing approach was made. Obtained results prove that the cellular neural network can be a valuable tool for infrared welding pool images segmentation.     

A method for moving objects segmentation based on human vision perception in infrared video

In this paper, a new region growing method to achieve the accurate and complete segmentation of the moving objects is introduced. Firstly, the ideal seeds of every moving object are extracted based on “hole” effect of temporal difference. Secondly, on the basis of the consideration that human vision system is most sensitive to the local contrast between targets and surrounding, we proposed a metric for “good” infrared target segmentation based on human vision perception. And according to this metric, a search method based on fine and rough adjustment is applied to determine the best growing threshold for moving objects. The segmented mask of every moving object is grown from the relevant seeds with the best growing threshold. At last, the segmented masks of all moving objects are merged into a complete segmented mask. Experimental results show that the proposed method is superior and effective on segmentation of infrared moving object.     

彩色分割的航空影像房屋自动检测

航空影像房屋提取方法的研究中大多基于灰度影像的区域生长算法,此类算法不仅忽略了不同材质的房屋所呈现的光谱特征对提取结果的影响,而且过于依赖种子像素的选取,处理效率不高。为了从高分辨率航空影像中实现房屋的自动检测,综合利用彩色信息与屋顶材料的光谱特征,采用影像分割原理,研究了房屋自动检测的方法。首先对RGB与HIS彩色空间进行转换,利用HIS空间各分量间不相关的特点和屋顶材料光谱特征进行影像分割,分离出红色瓦片屋顶与灰色水泥屋顶区域,并利用标记分水岭算法实现房屋区域的初始分割;然后计算各标记区域内的色调均值选取种子像斑样本,进而以像斑为单元在色调分量中进行区域生长,最后经过消除小斑和矩形拟合优化处理,得到轮廓清晰的房屋区域。与传统的基于像素区域分割算法相比,该方法整个过程无需人工干预且均在一维彩色空间进行处理,计算量明显降低,同时采用改进的基于像斑区域生长算法能够兼顾邻近区域内像素的几何结构信息,使算法精度得到显著提高,采用上述方法对高分辨率航空影像进行了实验,结果证明该方法有着较高的处理效率和准确性,具有实用价值。     

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。结论：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。     

Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing

Magnetic resonance imaging (MRI) is a valuable diagnostic tool in medical science due to its capability for soft-tissue characterization and three-dimensional visualization. One potential application of MRI in clinical practice is brain parenchyma classification and segmentation. Based on fuzzy knowledge and modified seeded region growing, this work proposes a novel image segmentation method, called Fuzzy Knowledge-Based Seeded Region Growing (FKSRG), for multispectral MR images. In this work, fuzzy knowledge includes the fuzzy edge, fuzzy similarity and fuzzy distance, which are obtained from relationships between pixels in multispectral MR images and are applied to the modified seeded regions growing process. In conventional regions merging, the final number of regions is unknown. Therefore, a Target Generation Process is proposed and applied to support conventional regions merging, such that the FKSRG method does not over- or undersegment images. Finally, two image sets, namely, computer-generated phantom images and real MR images, are used in experiments to assess the effectiveness of the proposed FKSRG method. Experimental results demonstrate that the FKSRG method segments multispectral MR images much more effectively than the Functional MRI of the Brain Automated Segmentation Tool, K-means and Support Vector Machine methods.     

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

Automated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability.     

一种基于区域特性的红外与可见光图像融合算法

提出了一种基于区域分割和à trous小波变换的红外与可见光图像融合算法.首先,对红外与可见光图像进行区域分割及区域关联,并按关联映射图所划分区域提取红外与可见光图像的的能量信息及梯度信息;然后,对红外与可见光图像进行多尺度à trous小波变换分解,分解后的低频部分按照文中所提出的区域能量比和区域清晰比指标进行区域融合,高频部分采用绝对值取大算子进行融合;最后进行重构得到融合图像.结果表明,该算法既可保持可见光图像的光谱信息,又可有效获取红外图像的热目标信息.     

多尺度无监督彩色图像分割

非采样Contourlet变换是一种新的多尺度多分辨率分析工具.本文提出了一种基于非采样Contourlet变换的彩色图像无监督分割算法.首先利用非采样Contourlet变换的平移不变性在其变换域应用梯度向量法提取图像多尺度边缘|然后在Contourlet变换域的低频子带和高频子带中分别提取局部低频能量纹理特征与高频多尺度Zernike矩纹理特征,并将二种纹理特征融合.最后在边缘图像中映射种子像素点,利用纹理和颜色特征欧氏距离,对彩色图像采用区域生长和区域合并的方法进行分割.实验结果证明:该算法将图像空间域的颜色特征与非采样Contourlet变换域的多尺度边缘和纹理特征恰当结合在一起实现彩色图像无监督自动分割,与传统算法相比有更高的准确性和鲁棒性.     

中药光谱成像图像自适应区域增长分割方法

为了消除背景噪声对药材光谱图像检测结果的干扰,根据中药材光谱图像的特点,设计一种能够自适应对中药材光谱图像进行有效区域(ROI)分割的区域增长算法。该区域增长算法根据药材光谱图像的灰度直方图分布来自动选取种子点和分割阈值,在生长的同时进行连通性分析,生长结束后通过区域填充技术来消除图像中出现的孔洞。实验表明:该方法能够自动、准确地进行ROI分割,分割偏差小于8%,并且能较好地消除噪声的干扰,没有产生无意义的生长区域。     

X光图像中缺陷的自动提取方法研究

针对炭素制品X光图像的特点,对其缺陷的提取技术进行了研究,提出了基于迭代的阈值构造方法和基于数学形态学的边缘提取算法。为快速准确地提取缺陷,设计了目标边界提取算法和基于小波变换的图像增强算法,实现了原始图像中目标区域的增强及其背景的去除。在此基础上,为排除噪声干扰的影响,采用数学形态学和迭代阈值分割相结合的方法从目标区域中提取出缺陷区域,并在迭代阈值分割的基础上,利用基于数学形态学的边缘提取算法提取了缺陷的边缘。实验结果表明,该法很好地实现了缺陷区域及其边缘的自动提取,且受噪声影响很小,为进一步的缺陷特征参量的提取与选择奠定了良好的基础。     

一种红外成像制导中的图像分割算法

红外成像制导导弹的图像分割是图像处理中的重点和难点之一。针对飞机目标红外图像的特点,提出了一种有利于目标特征点识别的图像分割算法。它首先采用区域填充的方法对图像进行分割,然后在此基础之上利用形态学原理对分割过程中产生的"洞"区域进行填充,最后得到比较精确的分割图像。对于实现红外成像制导导弹的稳定跟踪具有一定的应用价值。     

基于区域增长的图像跟踪算法的研究

为了提高序列图像的跟踪精度,提出了基于区域增长的特征量提取方法。这种方法可捕获图像中所有单连通域,并能准确地提取其特征量,然后依据所提取的特征量设计分类器,实现对图像中所有单连通域进行分类识别并加以跟踪的目的。此算法有效地解决了一般传统识别算法难以区分目标和其近邻区域的干扰,而导致跟踪目标特征量提取不准确的问题。在目标特征提取识别算法的基础上,还提出了阈值预测分割算法,并应用在序列图像的跟踪中,取得了较好效果。     

基于图的加权核K均值的图像多尺度分割

提出改进的最小割(IMC)模型以避免分割出小的孤立点集,研究了改进的最小割模型与加权核K均值之间的等价关系,列举了几种常见的用于建立图割模型边权值的相似度函数,并分析了其对分割结果的影响.在此基础上.设计了一个摹于图的加权核K均值图像多尺度分割方法,该方法既避免了基于图割的图像分割中图谱的求解问题,又避免了加权核K均值方法中核矩阵的选取问题,同时实现了对图像多尺度的分割.通过对该方法进行抗噪性能的分析,以及在光学图像上对实验结果进行比较,验证了所提出方法的有效性.     

一种视频运动目标精确分割新算法

针对视频图像中单个运动目标的分割问题,提出了一种基于Kirsch边缘算子的视频运动目标分割算法,该算法将Kirsch算子检测到的边缘作为主分割信息,运动矢量场作为次要分割信息。首先利用双重尺度的运动矢量场进行累加和滤波处理来获得辅助分割信息;然后将Kirsch算子的模板分解为差值模板和公共模板以提高边缘的抗噪性;最后用自适应状态标记的方法将边缘信息和运动矢量信息相融合来准确地分割运动目标。实验结果表明该方法分割比较精确。