超声图像处理中Snake模型研究

Snake模型是一种基于高层信息的有效目标轮廓提取算法,其优点是作用过程及最后结果的目标轮廓是一条完整的曲线,从而引起广泛的关注。鉴于医学超声图像的信噪比较低,用经典的边缘提取算法无法得到较好的结果,因此人们将Snake模型进行了各种各样的改进,并且越来越多地将它运用到医学超声图像处理中来。本文对乳腺超声图像进行阈值分割、形态滤波等一系列预处理后,将改进的Snake模型对乳腺超声图像进行肿瘤的边缘提取,得到了比较好的结果。     

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PET医学图像边缘检测算法的研究

目的:边缘检测在图像处理中至关重要,可被广泛应用于目标区域识别、区域形状检测、图像分割等图像分析领域。边缘是图像中不平稳现象和不规则结构的重要表现,往往携带着图像中的大量信息,并给出图像轮廓。在医学图像三维显示技术中,为了更精确的临床判别需要得到单像素的清晰轮廓,因此我们提出一种新的边缘检测算法。方法:在传统的小波边缘检测的基础上,提出了一种新的边缘算法,即基于小波极大值边缘检测算法,应用模糊算法构造相应的隶属函数,再对得到的极大值进一步筛选。结果:将该算法应用到医学图像中,最终可以得到较清楚的单像素边缘轮廓,实验结果证明了该算法的可行性。结论:运用这种算法处理过的医学图像边缘锐化更好,更清晰,能够为肿瘤的早期识别提供依据,满足医学影像识别的需要。     

基于启发式A^＊算法的超声图像颈动脉内膜提取

从超声图像准确提取颈动脉内膜,为基于颈动脉超声图像判断动脉粥样硬化服务。方法提出一种基于启发式A*算法从超声图像中提取颈动脉内膜边缘的方法。先使用图像分割法区分血管腔和血管壁,再采用结合图像灰度值特点的A*算法准确地提取颈动脉内膜边缘。结果通过对临床采集的32幅颈动脉超声图像的分析研究,表明本方法自动提取的结果与医生手工描绘的结果基本吻合。结论本方法有望应用于超声图像颈动脉内膜的自动提取。     

医学图像中的边缘分析

根据医学图像处理的要求,需要将图像划分若干区域,其划分过程要求迅速、精确。本文结合实际经验,介绍了图像分割的重要方法——边缘提取,并着重分析了其中边缘检测和边缘跟踪的过程和方法,同时还给出了用计算机模拟得到的边缘提取的结果。     

基于改进C-V模型的超声心动图像轮廓的提取方法

目的:通过超声图像预处理和对图像分割方法的改进,完成超声心动图中心腔轮廓的提取。方法:首先,运用基于斑点指数的滤波方法对超声图像进行去噪。其次,对超声图像进行分段非线性灰度变换,提高图像对比度。最后,利用改进的基于C-V模型的水平集算法对超声图像进行分割,得到精确的初始轮廓。结果:1基于斑点指数的图像滤波方法可以在不丢失细节的情况下对超声图像进行噪声滤除。2分段非线性灰度变换可以有效提高超声图像的对比度。3改进的C-V模型可以成功的对含有斑点噪声的超声图像进行分割。结论:本文的超声图像预处理方法和分割算法可以有效提取心腔轮廓,降低斑点噪声对图像分割结果的影响。     

基于动态方向梯度矢量流模型的脑肿瘤图像分割

目的:针对GVF Snake模型算法收敛容易陷入局部极小值及对初始轮廓位置敏感等缺点,提出一种动态方向梯度矢量流模型(DDGVF),使其更适合医学图像的分割。方法:利用主动轮廓模型的提取和跟踪特定区域内目标轮廓的方法,将其应用于医学图像如CT、MRI和超声图像的处理,以获取特定器官及组织的轮廓。结果:动态方向梯度矢量流场(DDGVF)能够较好地提取出脑肿瘤图像。结论:利用该方法能够较好地分割提取出脑肿瘤图像的肿瘤病变区域,为进一步对其纹理和形状等特征进行描述和分析提供了可靠的依据。     

基于多小波的胃癌病理细胞图像边缘检测与分析

对胃癌细胞图像的多尺度小波变换边缘检测进行了研究,为医生运用现代信息理论的方法进行相关疾病诊断提供了一种新的思路和途径。提出了多尺度小波边缘检测的新方法,归纳了改善小波边缘检测效果的一些策略。实验结果表明,对于具有复杂纹理的医学病理细胞图像,采用传统的边缘检测方法会产生伪边缘和方向性误差,它影响了图像边缘检测的可信度;而运用小波变换的时频尺度特性和对奇异变化的优良检测性能,可得到无噪声污染的图像实际边缘。     

不同滤波方法对超声图像分割的影响

采用各向异性滤波方法以及Gabor滤波方法对乳腺肿瘤超声图像进行处理,再使用snake方法以及levelset方法在设置相同参数的条件下,对过滤的图像分别进行分割。试验结果表明,不同的滤波方法在分害4目标图像的收敛度,边缘圆滑度及整体轮廓提取效果都有较大影响,为分割图像选取适当的滤波器提供了参考。     

鼻咽癌细胞双光子显微图像处理

鼻咽细胞的双光子显微图像中含有着丰富信息,借助计算机和图像处理算法可进行分析处理。图象分割是双光子显微图象处理中的一项重要技术,至今为止尚未形成一个最佳通用方法,也没有定义出双光子显微图象分割的统一标准。本文首先采用噪声干扰法进行去噪,采用低帽的变换等的数学形态学来增强鼻咽癌细胞图像,使细胞更加容易分辨,接着对几种经典边缘检测算法进行讨论比较,紧接着根据鼻咽双光子显微图像的实际特征,采取腐蚀算法求出鼻咽癌细胞边缘。然后进行区域生长定位细胞,并采用一些改进的判别分析算法和区域面积算法对鼻咽癌细胞进行阈值分割,获得较好结果。     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

A new registration method for three-dimensional knee nearthrosis model using two X-ray images

The purpose of this study is to develop a method to analyse the pose of the knee nearthrosis mounted and to automate the registration procedure for easy use in clinical applications. The proposed registration method is essentially a model-based method, in which the CAD model is acquired by reverse engineering. The CAD model is converted into a two-dimensional (2D) image by a rendering technique, and the compatibility of the X-ray image and the image of the CAD model is investigated. To avoid the optimisation of six unknown parameters with respect to the relative pose between the condyle and tibial models, a 2D coordinate system is set on each component of the X-ray images. A 3D coordinate system is also set on each of the two nearthrosis components. With such a setup, there is only one unknown rotational angle on each component, which is determined by an optimum algorithm in accordance with the contour error between the X-ray image and the image of the CAD model. Extensive computer simulation and in vitro experiments using real X-ray images have been implemented to investigate the feasibility of the proposed registration method.     

Technique for chestband contour shape-mapping in lateral impact

The chestband transducer permits noninvasive measurement of transverse plane biomechanical response during blunt thorax impact. Although experiments may reveal complex two-dimensional (2D) deformation response to boundary conditions, biomechanical studies have heretofore employed only uniaxial chestband contour quantifying measurements. The present study described and evaluated an algorithm by which source subject-specific contour data may be systematically mapped to a target generalized anthropometry for computational studies of biomechanical response or anthropomorphic test dummy development. Algorithm performance was evaluated using chestband contour datasets from two rigid lateral impact boundary conditions: Flat wall and anterior-oblique wall. Comparing source and target anthropometry contours, peak deflections and deformation-time traces deviated by less than 4%. These results suggest that the algorithm is appropriate for 2D deformation response to lateral impact boundary conditions.     

Magnetic microbubble-mediated ultrasound-MRI registration based on robust optical flow model

Background

As a dual-modality contrast agent, magnetic microbubbles (MMBs) can not only improve contrast of ultrasound (US) image, but can also serve as a contrast agent of magnetic resonance image (MRI). With the help of MMBs, a new registration method between US image and MRI is presented.

Methods

In this method, MMBs were used in both ultrasound and magnetic resonance imaging process to enhance the most important information of interest. In order to reduce the influence of the speckle noise to registration, semi-automatic segmentations of US image and MRI were carried out by using active contour model. After that, a robust optical flow model between US image segmentation (floating image) and MRI segmentation (reference image) was built, and the vector flow field was estimated by using the Coarse-to-fine Gaussian pyramid and graduated non-convexity (GNC) schemes.

Results

Qualitative and quantitative analyses of multiple group comparison experiments showed that registration results using all methods tested in this paper without MMBs were unsatisfactory. On the contrary, the proposed method combined with MMBs led to the best registration results.

Conclusion

The proposed algorithm combined with MMBs contends with larger deformation and performs well not only for local deformation but also for global deformation. The comparison experiments also demonstrated that ultrasound-MRI registration using the above-mentioned method might be a promising method for obtaining more accurate image information.

     

应用菌紫质膜模拟动物“边”感受野并检测图像边界轮廓

菌紫质膜是一种颇有前途的分子电子器件材料。Tsutornu^[1]以菌紫质膜为材料,完成了具有动物视觉功能特点的含256像素的图像传感器。此外,菌紫质膜还被用来模拟视觉感受野的运算功能^[2-4]简单细胞“边”感受野由两个互为颉顽的ON响应区和OFF响应区组成。它对图像中的对比度信息具有敏感性。菌紫质膜因其优良的分辨率(5000线/min),灵敏度(1-805/cm²)以及光电特性而足以被用来制作人工“边”感受野,并模拟简单细胞“边”感受野的这一特性。以此为基础,本文构建了一个图像轮席检测的原理系统,成功地检测了简单图像轮廊。     

An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion

Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation.By two-scale image decomposition,the input image is decomposed into base layer and detail layer.For the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge information.Besides,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization problems.For the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain.The sum of the above base and detail layers is as the final fused image.Experimental results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.     

Automatic segmentation of cell nuclei in Feulgen-stained histological sections of prostate cancer and quantitative evaluation of segmentation results

Digital image analysis of cell nuclei is useful to obtain quantitative information for the diagnosis and prognosis of cancer. However, the lack of a reliable automatic nuclear segmentation is a limiting factor for high-throughput nuclear image analysis. We have developed a method for automatic segmentation of nuclei in Feulgen-stained histological sections of prostate cancer. A local adaptive thresholding with an object perimeter gradient verification step detected the nuclei and was combined with an active contour model that featured an optimized initialization and worked within a restricted region to improve convergence of the segmentation of each nucleus. The method was tested on 30 randomly selected image frames from three cases, comparing the results from the automatic algorithm to a manual delineation of 924 nuclei. The automatic method segmented a few more nuclei compared to the manual method, and about 73% of the manually segmented nuclei were also segmented by the automatic method. For each nucleus segmented both manually and automatically, the accuracy (i.e., agreement with manual delineation) was estimated. The mean segmentation sensitivity/specificity were 95%/96%. The results from the automatic method were not significantly different from the ground truth provided by manual segmentation. This opens the possibility for large-scale nuclear analysis based on automatic segmentation of nuclei in Feulgen-stained histological sections.     

Segmentation of left ventricle in short-axis echocardiographic sequences by weighted radial edge filtering and adaptive recovery of dropout regions

In this paper, we present a weighted radial edge filtering algorithm with adaptive recovery of dropout regions for the semi-automatic delineation of endocardial contours in short-axis echocardiographic image sequences. The proposed algorithm requires minimal user intervention at the end diastolic frame of the image sequence for specifying the candidate points of the contour. The region of interest is identified by fitting an ellipse in the region defined by the specified points. Subsequently, the ellipse centre is used for originating the radial lines for filtering. A weighted radial edge filter is employed for the detection of edge points. The outliers are corrected by global as well as local statistics. Dropout regions are recovered by incorporating the important temporal information from the previous frame by means of recursive least squares adaptive filter. This ensures fairly accurate segmentation of the cardiac structures for further determination of the functional cardiac parameters. The proposed algorithm was applied to 10 data-sets over a full cardiac cycle and the results were validated by comparing computer-generated boundaries to those manually outlined by two experts using Hausdorff distance (HD) measure, radial mean square error (rmse) and contour similarity index. The rmse was 1.83 mm with a HD of 5.12 ± 1.21 mm. We have also compared our results with two existing approaches, level set and optical flow. The results indicate an improvement when compared with ground truth due to incorporation of temporal clues. The weighted radial edge filtering algorithm in conjunction with adaptive dropout recovery offers semi-automatic segmentation of heart chambers in 2D echocardiography sequences for accurate assessment of global left ventricular function to guide therapy and staging of the cardiovascular diseases.     

Segmentation of left ventricle in short-axis echocardiographic sequences by weighted radial edge filtering and adaptive recovery of dropout regions

In this paper, we present a weighted radial edge filtering algorithm with adaptive recovery of dropout regions for the semi-automatic delineation of endocardial contours in short-axis echocardiographic image sequences. The proposed algorithm requires minimal user intervention at the end diastolic frame of the image sequence for specifying the candidate points of the contour. The region of interest is identified by fitting an ellipse in the region defined by the specified points. Subsequently, the ellipse centre is used for originating the radial lines for filtering. A weighted radial edge filter is employed for the detection of edge points. The outliers are corrected by global as well as local statistics. Dropout regions are recovered by incorporating the important temporal information from the previous frame by means of recursive least squares adaptive filter. This ensures fairly accurate segmentation of the cardiac structures for further determination of the functional cardiac parameters. The proposed algorithm was applied to 10 data-sets over a full cardiac cycle and the results were validated by comparing computer-generated boundaries to those manually outlined by two experts using Hausdorff distance (HD) measure, radial mean square error (rmse) and contour similarity index. The rmse was 1.83 mm with a HD of 5.12 ± 1.21 mm. We have also compared our results with two existing approaches, level set and optical flow. The results indicate an improvement when compared with ground truth due to incorporation of temporal clues. The weighted radial edge filtering algorithm in conjunction with adaptive dropout recovery offers semi-automatic segmentation of heart chambers in 2D echocardiography sequences for accurate assessment of global left ventricular function to guide therapy and staging of the cardiovascular diseases.     

An edge relocation segmentation algorithm

An automated procedure that refines the nuclear contour of a previously segmented nucleus is described. The algorithm makes use of intensity information, edge magnitude information and both object and edge connectivity information. This automated procedure generates a closed contour precisely along the edge of the nucleus. The procedure was tested on a database of 3,680 red-green-blue images of thionin-SO2 and orange II-stained cervical cells obtained from normal and dysplastic samples. When used in conjunction with a simple threshold selection algorithm and an artifact removal routine, this edge relocation algorithm resulted in the correct segmentation of over 98% of the nuclei. Only 63 (1.7%) of all nuclei were incorrectly segmented.