奇异值分解滤波联合Frangi滤波对超微血管成像图像质量的影响

目的 观察奇异值分解(SVD)滤波联合Frangi滤波对超微血管成像(SMI)图像质量的影响。方法 分别对1具仿体及1例疑诊肝血管瘤患者行CEUS,分别以SVD滤波(A组)、Frangi滤波(B组)、Frangi+SVD滤波(C组)及SVD+Frangi滤波(D组)处理图像,之后比较不同图像的质量。结果 4组仿体及人体肝脏SMI对比组织比(CTR)、对比度噪声比(CNR)及信噪比(SNR)均高于原始CEUS图像。相比原始图像,D组图像质量及分辨率提升最为显著,仿体SMI的 CTR、CNR及SNR分别提升58.04、3.39及48.04 dB,人体肝脏SMI相应参数则分别提升61.85、16.80及49.67 dB,而分辨率分别为原始CEUS图像的1.42倍及1.98倍。结论 SVD滤波联合Frangi滤波可有效提高SMI图像质量。     

SAR target classification using multi-aspect multi-feature collaborative representation

ABSTRACT

In this paper, a novel approach is proposed for Synthetic Aperture Radar (SAR) target classification based on multi-aspect multi-feature collaborative representation. Firstly, principal component analysis (PCA), wavelet and 2-dimensional slice Zernike moments (2DSZM) features are extracted from SAR images. Next, based on the strong correlation among the adjacent aspect SAR target images, we extend the basic collaborative representation classification (CRC) model to a neighbourhood multi-aspect CRC model. For each feature of the current test sample, neighbourhood multi-aspect test samples are regarded as the input to the model, then the temporary label is obtained for the current test sample under this feature. Finally, the temporary label is fused using the voting method to get the final classification result. The novelty of the proposed method is to improve the performance of target classification by integrating representation learning ability of different features and exploiting neighbourhood multi-aspect correlation. Experiments are investigated on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The results show that the proposed algorithm can achieve a 98.52% overall accuracy and is superior to state-of-the-art methods for SAR target classification.     

A variational change detection method for multitemporal SAR images

In this letter, we develop a variational model for change detection in multitemporal synthetic aperture radar (SAR) images. SAR images are typically polluted by multiplicative noise, therefore ordinary active contour model (ACM), or the snake model, for image segmentation is not suitable for change detection in multitemporal SAR images. Our model is a generalization of ACM under the assumption that the image data fits the Generalized Gaussian Mixture (GGM) model. Our method first computes the log-ratio image of the input multitemporal SAR images. Then the method iteratively executes the following two steps until convergence: (1) estimate the parameters for the generalized Gaussian distributions inside and outside the current evolving curve using maximum-likelihood estimation; (2) evolve the current curve according to the image data and the parameters previously estimated. When convergence is achieved, the location of the evolving curve depicts the changed and the unchanged areas.

Experiments were carried out on both semi-simulated data set and real data set. Results showed that the proposed method achieves total error rates of 0.43% and 1.05%, for semi-simulated and real data sets, respectively, which were comparable to other prevalent methods.     

CT图像中肺实质的自动分割

目的 为解决肺实质分割中肺部结节及高密度血管易遗漏的问题,提出一种自动肺实质分割方法.方法 首先利用二维区域生长反操作、连通区域判别等方法提取肺实质区域;然后利用行扫描法定位肺区边界点;最后通过对边界点参数分析,定位受肿瘤侵占的边界点,利用曲线拟合修复受损边界.结果 通过对多组胸部CT图像的分割,验证了算法的有效性;与几种常见边界修复算法对比,验证了行扫描边界修复算法的优越性.结论 本文提出的算法能将肿瘤包含到肺实质区域,确保分割的完整性、准确性、实时性.     

3D multi-scale vessel enhancement filtering based on curvature measurements: application to time-of-flight MRA

In this paper we evaluate the use of voxel intensity curvature measurements to enhance vessels in 3D MRA images. We compare a multi-scale discrete kernel filter (MaxCurve) to the Hessian matrix based filter proposed by Frangi and co-workers. The MaxCurve filter is based on the maximum difference between the negative curvature computed along orthogonal lines defined by a 3x3x3 kernel. Filter performance is assessed using measures of vessel and background separation (contrast and the area under the ROC curve). Filter parameters are optimized using a training set of four typical time-of-flight MRA images and tested on a separate set of ten MRA images with the same acquisition parameters. The filters tended to provide good MIP image contrast enhancement. The filters are applied to MRA images acquired with different parameters and field strengths indicating potential usefulness for a variety of images. Overall the discrete kernel and Hessian matrix filter performed quite similarly.     

Fine-grained ship classification based on deep residual learning for high-resolution SAR images

ABSTRACT

As the resolution of Synthetic Aperture Radar (SAR) images increases, the fine-grained classification of ships has become a focus of the SAR field. In this paper, a ship classification framework based on deep residual network for high-resolution SAR images is proposed. In general, networks with more layers have higher classification accuracy. However, the training accuracy degradation and the limited dataset are major problems in the training process. To build deeper networks, residual modules are constructed and batch normalization is applied to keep the activation function output. Different fine tuning strategies are used to select the best training scheme. To take advantage of the proposed framework, a dataset including 835 ship slices is augmented by different multiples and then used to validate our method and other Convolutional Neural Network (CNN) models. The experimental results show that, the proposed framework can achieve a 99% overall accuracy on the augmented dataset under the optimal fine-tuning strategy, 3% higher than that in other models, which demonstrates the effectiveness of our proposed approach.     

Selection of multi-view SAR images via nonlinear correlation information entropy with application to target classification

ABSTRACT

A synthetic aperture radar (SAR) target classification method is proposed by properly selecting multiple views via the nonlinear correlation information entropy (NCIE). The optimal subset of multi-view SAR images are selected, which are assumed to share stable inner correlations. The joint sparse representation is adopted as the basic classification scheme for the selected views to exploit their inner correlations. According to the total reconstruction error of the selected multi-view SAR images, the target label can be decided. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is employed to test the proposed method and the results validate the its superiority over some reference methods.     

一种半自动分割脑部MRI中胼胝体的方法

针对胼胝体的图像特点以及实际应用要求,采用半自动方法对MRI中的胼胝体进行分割。首先采用基于Live-Wire的算法对胼胝体影像的起始层和终止层进行初始分割,然后利用基于距离变换的形状插值算法获取中间层的初始轮廓信息,对插值获得的初始轮廓采用Snake模型进行局部收缩,获得真实的胼胝体边界。对序列MRI脑影像中的胼胝体进行分割、重建、标定。实验结果与临床医师的使用反馈证明,本文提出的算法具有较高的灵活性与可信度,对胼胝体的分割精度与解剖统计信息相符,分割结果可满足临床需求。     

Segmentation of lumen and outer wall of abdominal aortic aneurysms from 3D black-blood MRI with a registration based geodesic active contour model

Segmentation of the geometric morphology of abdominal aortic aneurysm is important for interventional planning. However, the segmentation of both the lumen and the outer wall of aneurysm in magnetic resonance (MR) image remains challenging. This study proposes a registration based segmentation methodology for efficiently segmenting MR images of abdominal aortic aneurysms. The proposed methodology first registers the contrast enhanced MR angiography (CE-MRA) and black-blood MR images, and then uses the Hough transform and geometric active contours to extract the vessel lumen by delineating the inner vessel wall directly from the CE-MRA. The proposed registration based geometric active contour is applied to black-blood MR images to generate the outer wall contour. The inner and outer vessel wall are then fused presenting the complete vessel lumen and wall segmentation. The results obtained from 19 cases showed that the proposed registration based geometric active contour model was efficient and comparable to manual segmentation and provided a high segmentation accuracy with an average Dice value reaching 89.79%.     

Differentiation of myocardial infarction and angina pectoris by processing ultrasonic color kinesis images

Purpose The aim of this study was to develop a method for early, accurate differentiation between old myocardial infarction (OMI) and angina pectoris (AP) using color kinesis (CK) images. We first extracted exact end-diastolic and end-systolic contours from CK images and then extracted the features of cardiac function from two CK images (one at rest, the other after exercise) and investigated their effectiveness in differentiating old myocardial infarction and angina pectoris. We then evaluated the effectiveness of several features in recognizing coronary artery disease and used the effective features to show the differentiation results.Methods First, we extracted exact end-diastolic and end-systolic contours from CK images with an active contour model. Second, we defined the features that seemed to be effective in recognizing coronary artery disease. The features are extracted from the region between the end-diastolic endocardial contour and end-systolic endocardial contour in two CK images: one obtained when the subject was at rest and the other after exercise. Nine features were considered effective for differentiating old myocardial infarction and angina pectoris, and the effectiveness in recognizing coronary artery disease, which includes old myocardial infarction and angina pectoris, was evaluated. Third, coronary artery disease is recognized by the effective features.Results Contours near a manual trace by a skilled physician were obtained using the proposed method. Multiple comparisons of the mean values of the extracted features were drawn among three groups: a healthy-subject group; an old myocardial infarction patient group; and an angina pectoris patient group. The feature effective in differentiating old myocardial infarction was the “area at rest”; those effective in differentiating angina pectoris were a “decrease in area” and a “decrease in movement.” These effective features have almost always differentiated old myocardial infarction and angina pectoris.Conclusions This study used the endocardial contour extraction technique with the dynamic contour model and evaluated the validity of the features of cardiac function; it then recognized coronary artery disease from the effective features. Multiple comparisons of the mean value of the extracted features among the healthy-subject group, the old myocardial infarction patient group, and the angina pectoris patient group has proved that the “area at rest” is effective in differentiating old myocardial infarction, and the “decrease in area” and “decrease in movement” are effective for differentiating angina pectoris.     

A framework for automatic analysis of the dynamic behaviour of coronary angiograms

A framework for coronary vessels analysis in digital subtracted angiograms is described. This method combines the motion estimation with the frame-to-frame structure detection in a natural way such that they act interactively. The first step consists of the extraction of the vessel centrelines in one image and their organization into meaningful constituents or branches of the coronary arterial tree. The motion is then estimated along the centrelines through a gradient based method. These motion estimates supply an initial positioning of an active contour model (or ‘snake“) in the next image. This model adapts itself by changing its shape to accurately fit onto the new centrelines. This process is then reiterated on the subsequent images to depict the dynamic behaviour of all the relevant branches. The main interests of this scheme are: (1) the active models operate locally so a fast detection of the vessels can be performed; (2) the centrelines extraction is fully guided by the confluence of the motion estimation and the contour model; (3) both morphological and kinetic features are provided on a quantitative basis.     

Segmentation of breast ultrasound images based on active contours using neutrosophic theory

Purpose

Ultrasound imaging is an effective approach for diagnosing breast cancer, but it is highly operator-dependent. Recent advances in computer-aided diagnosis have suggested that it can assist physicians in diagnosis. Definition of the region of interest before computer analysis is still needed. Since manual outlining of the tumor contour is tedious and time-consuming for a physician, developing an automatic segmentation method is important for clinical application.

Methods

The present paper represents a novel method to segment breast ultrasound images. It utilizes a combination of region-based active contour and neutrosophic theory to overcome the natural properties of ultrasound images including speckle noise and tissue-related textures. First, due to inherent speckle noise and low contrast of these images, we have utilized a non-local means filter and fuzzy logic method for denoising and image enhancement, respectively. This paper presents an improved weighted region-scalable active contour to segment breast ultrasound images using a new feature derived from neutrosophic theory.

Results

This method has been applied to 36 breast ultrasound images. It generates true-positive and false-positive results, and similarity of 95%, 6%, and 90%, respectively.

Conclusion

The purposed method indicates clear advantages over other conventional methods of active contour segmentation, i.e., region-scalable fitting energy and weighted region-scalable fitting energy.

     

双源CT心脏图像中二尖瓣的分割及三维重建

目的 对双源CT(DSCT)图像中心脏二尖瓣进行分割和三维重建,为二尖瓣结构和功能异常分析提供参考。 方法 采用两步分割法对DSCT图像中二尖瓣分割:首先利用基于区域竞争主动轮廓模型的快速水平集算法(RCAC-FLSA)对经过双边滤波处理后图像进行初步分割,得到心脏对比剂增强区域;然后在灰度拉伸处理的基础上,结合ROI,再次利用RCAC-FLSA对上一步分割结果进行分割,得到心脏二尖瓣区域;最后对二尖瓣进行恢复。在Visual C++ 2005平台上结合OpenGL开发三维重建与显示平台,利用基于三维纹理映射的体绘制方法进行三维重建,并且加入伪彩色处理和透明度处理,以增强三维重建的立体效果。 结果 成功分割出一系列DSCT心脏图像中的二尖瓣,结合伪彩色处理和透明度处理的三维重建与显示平台,可获得二尖瓣的逼真重建。 结论 两步分割算法能有效分割DSCT心脏图像中的二尖瓣;结合伪彩色处理和透明度处理的三维重建与显示平台,能提供逼真的三维重建效果。     

MRI-based microrobotic system for the propulsion and navigation of ferromagnetic microcapsules

Abstract

This paper presents real-time MRI-based control of a ferromagnetic microcapsule for endovascular navigation. The concept was studied for future development of microdevices designed to perform minimally invasive interventions in remote sites accessible through the human cardiovascular system. A system software architecture is presented illustrating the different software modules to allow 3-D navigation of a microdevice in blood vessels, namely: (i) vessel path planner, (ii) magnetic gradient steering, (iii) tracking and (iv) closed-loop navigation control. First, the position recognition of the microrobot into the blood vessel is extracted using Frangi vesselness filtering from the pre-operation images (3-D MRI imaging). Then, a set of minimal trajectories is predefined, using path-planning algorithms, to guide the microrobot from the injection point to the tumor area through the anarchic vessel network. Based on the pre-computed path, a Generalized Predictive Controller (GPC) is proposed for robust time-multiplexed navigation along a two-dimensional (2D) path in presence of pulsative flow.     

Directional feature: a novel feature for group target detection in high resolution SAR images

In this letter, we propose a new feature for group target detection in high resolution Synthetic Aperture Radar (SAR) images. This study aims to reduce the false alarm rate by adding a novel directional feature to typical SAR target detection algorithms. Unlike other shape- or contrast-based features, the directional feature contains the orientation and angle information of targets. Based on this feature, we can distinguish group targets from false alarms by analysing the correlation of their directional features. First, the proposed feature extraction approach generates an enhanced image to overcome speckle noises and a low signal to noise ratio (SNR). Next, a contour extraction algorithm is used to generate a line-drawing edge map of the target. Finally, the directional feature is obtained based on the major principal axes of the edge map. The experimental results using real high resolution SAR images verify the validity and effectiveness of the SAR detection algorithm.     

区域生长法结合多竞争最小二乘拟合算法去除乳腺X线摄影图像中胸大肌影

目的 评价区域生长法结合多竞争最小二乘拟合算法去除数字乳腺X线摄影（MG）图像中胸大肌影的价值。方法 分层抽样法随机抽取244例MG数据,对图像进行轮廓选择、增强数据特征、胸大肌边界轮廓粗定位和去噪处理;结合最小二乘法改进区域生长法,拟合胸大肌的边界轮廓函数,使用最优轮廓函数制作胸大肌掩膜图,计算预测图与人工勾画图交并比（IOU）及像素精度（PA）,评价其去除MG图像中的胸大肌影的价值。结果 基于上述方法所获胸大肌轮廓较为平滑,较少漏分割或过度分割,结果误差较小;还原胸大肌边界轮廓与手动分割结果非常接近,平均IOU为（89.76±4.28）%,平均PA为（89.98±3.91）%。结论 结合区域生长法与多竞争最小二乘拟合算法可用于去除MG图像中的胸大肌影。     

Representation of BVMD features via multitask compressive sensing for SAR target classification

ABSTRACT

This letter develops a synthetic aperture radar (SAR) target classification method based on bidimensional variational mode decomposition (BVMD) and multitask compressive sensing (MTCS). BVMD is employed to decompose SAR images to exploit the time-frequency properties of the described targets. The MTCS is used to jointly classify the original SAR image and its BVMD components. So, the merits of BVMD and MTCS can be combined in the proposed method. Finally, based on the reconstruction errors, the target label can be decided. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used to set up experimental conditions to test the proposed method. By comparison with several reference methods from published works, the effectiveness and robustness of the proposed method can be confirmed.     

基于相位信息的乳腺超声图像水平集分割

目的基于相位信息改进距离正规化水平集演化(DRLSE)模型的速度收敛项,改善对乳腺肿瘤超声图像的分割效果。方法首先,利用Log-Gabor滤波器组对图像进行频域滤波,得到一组基于相位信息的特征图。其次,在相位一致性的基础上,求出乳腺超声图像经高斯噪声补偿后的最大方向能量相位PC(M),并采用细节保留各向异性扩散滤波(DPAD)模型对PC(M)降噪,减少斑点噪声的干扰。最后,选用Sigmoid函数,将滤波后的PC(M)作为其自变量,以替换DRLSE模型中的速度收敛项。结果采用改进后的模型对多幅临床乳腺肿瘤超声图像进行分割,分割结果显示基于相位信息的正规化水平集演化(PB-DRLSE)模型在相似性(SI)、真阳性(TP)和假阴性(FN)方面均优于原始DRLSE模型(P均<0.05)。结论本研究提出的分割方法较之原始模型对乳腺肿瘤超声图像的分割更为优越。     

基于改进移动立方体算法的三维重建

目的 探讨基于改进移动立方体算法的腹部器官CT图像三维重建效果。方法 提出一种基于区域增长法的通用树结构和移动等值点法的自适应改进移动立方体算法,先进行医学图像分割,选取种子点后标记出与阈值相交的所有体元;创建通用树结构,将相交体元插入子节点中,确定基于通用树的顶点索引方式;通过移动等值点合并共面三角形,简化等值点信息的获取。基于1名志愿者的腹部CT图像,采用传统移动立方体算法和改进移动立方体算法构建肾脏三维模型,并比较其效果。结果 与传统算法比较,改进的移动立方体算法生成的三角面片个数减少39.20%,算法执行效率提高37.59%,三维模型表面平滑逼真,局部细节真实性较好。结论 基于改进移动立方体的算法可更快速精确地实现CT图像腹部器官三维重建。     

SAR image classification based on alpha-stable distribution

We propose a classification algorithm that utilizes the alpha-stable distribution to model the texture features of synthetic aperture radar (SAR) images. The SAR image is first decomposed by stationary wavelet transform (SWT). After that, the alpha-stable distribution is applied to model the high-frequency subband coefficients of the image at each decomposition scale. A regression-type method is then used to estimate the alpha-stable distribution parameters, which form a feature vector that fully describes the texture. Finally, a SAR image classification algorithm is derived by exploiting this feature vector based on the support vector machines (SVM) approach. Because different combinations of alpha-stable distribution parameters contribute to differences in classification precision, a multilevel SVM (MSVM) classification algorithm is also presented to address the issue. Experimental results indicate that the proposed SAR image classification algorithm is effective and the MSVM algorithm improves the classification performance. Moreover, our proposed algorithm has low computational cost as only a small number of the alpha-stable distribution parameters are processed.