Completely automated multiresolution edge snapper--a new technique for an accurate carotid ultrasound IMT measurement: clinical validation and benchmarking on a multi-institutional database

The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management.     

Segmentation in Ultrasonic B-Mode Images of Healthy Carotid Arteries Using Mixtures of Nakagami Distributions and Stochastic Optimization

The goal of this work is to perform a segmentation of the intimamedia thickness (IMT) of carotid arteries in view of computing various dynamical properties of that tissue, such as the elasticity distribution (elastogram). The echogenicity of a region of interest comprising the intima-media layers, the lumen, and the adventitia in an ultrasonic B-mode image is modeled by a mixture of three Nakagami distributions. In a first step, we compute the maximum a posteriori estimator of the proposed model, using the expectation maximization (EM) algorithm. We then compute the optimal segmentation based on the estimated distributions as well as a statistical prior for disease-free IMT using a variant of the exploration/selection (ES) algorithm. Convergence of the ES algorithm to the optimal solution is assured asymptotically and is independent of the initial solution. In particular, our method is well suited to a semi-automatic context that requires minimal manual initialization. Tests of the proposed method on 30 sequences of ultrasonic B-mode images of presumably disease-free control subjects are reported. They suggest that the semi-automatic segmentations obtained by the proposed method are within the variability of the manual segmentations of two experts.      

基于HEA算法的二维最大相关准则图像分割

针对目前一些算法对于同一图像每次分割得出的阈值不同,存在分割不稳定的问题,提出了一种基于和声五元素算法(HEA)的二维最大相关准则图像分割算法。该算法利用图像目标和背景分布的相关量最大值来选取阈值,并运用 HEA算法优秀的全局搜索能力优化运算速度,提高图像分割的稳定性和质量。实验结果表明本文方法有较好的稳定性,分割效果良好。     

Cross-term elimination in Wigner distribution based on 2D signal processing techniques

An efficient method based on 2D signal processing techniques and fractional Fourier transform is presented to suppress interference terms of Wigner distribution (WD). The proposed technique computes Gabor transform (GT) of a multi-component signal to obtain a blurred time-frequency (t-f) image. Signal components in GT image are segmented using connected component segmentation and are filtered out using precise application of fractional Fourier transform. A crisp t-f representation is then obtained by computing the sum of products of WD and GT of the isolated signal components. The efficacy of the proposed technique is demonstrated using examples of synthetic signals and real-life bat signals. Proposed scheme gives satisfactory performance even when cross-terms of WD overlap auto-terms and computational cost analysis shows that it is more efficient than recent interference suppression techniques of comparable performance. Moreover, the proposed technique does not require any prior info regarding the nature of signal.     

免疫克隆SAR图像分割算法

由于存在相干斑噪声的影响,使得常规的图像分割技术应用于SAR图像时,效果往往较差。该文提出一种新人工免疫系统SAR图像分割算法,算法首先提取每幅图像的纹理特征,并对其进行实数编码,然后通过借鉴生物学免疫系统的抗体克隆选择机理,构造适合于图像分割的克隆算子,以较快的收敛速度优化目标函数。实验结果表明,新算法是一种有效的SAR 图像区域分割方法。     

Tamper video detection and localization using an adaptive segmentation and deep network technique

In this work we have explored the hybrid deep learning architecture for recognizing the tampering from the videos. This hybrid architecture explores the features from the authentic videos to categorize the tampered portions from the forged videos. Initially, the process begins by compressing the input video using the Discrete cosine transform (DCT) based double compression approach. Then, the filtering process is carried out to improve the quality of compressed frame using the bilateral filtering. Then, the modified segmentation approach is applied to segment the frames into different regions. The features from these segmented portions are extracted and fed into hybrid DNN-AGSO (deep neural network- Adaptivf RELATED WORKSe Galactic Swarm Optimization) using Gabor wavelet transform (GWT) technique. Three different datasets are used to evaluate the overall performance they are, VTD, MFC-18, and VIRAT by MATLAB platform. The recognition rate achieved by VTD, MFC-18, and VIRAT datasets are 96%, 95.2%, and 93.47% respectively.     

高分辨SAR复杂场景中的人造目标检测

合成孔径雷达成像的最终目的是对成像场景中感兴趣的目标进行有效的检测、识别和监视。该文针对实际复杂地面环境场景中的人造目标检测问题,提出一种有效的SAR图像目标检测方法。该方法提出一种基于信息熵的改进Lee滤波算法,在图像平滑的同时,保持图像的细节和邻域特征信息;然后,采用门限化分割技术对高分辨SAR图像中的目标进行粗略检测;最后采用方向梯度能量分形特征提取方法,对图像中目标的尺寸和形状进行准确的估计,实现对目标的精确检测。实测数据的处理结果验证了该方法的有效性。     

视网膜OCT B-scan图像分割算法综述

依靠光学相干层析(OCT)技术能够获得清晰的视网膜结构图,对于视网膜病变的诊断具有重要的意义。文章回顾了既往研究中典型的视网膜OCT B-scan图像分割算法,根据算法的输出特征,将其分为视网膜边界提取法和视网膜层提取法两类,旨在明确不同的方法获取信息的差异性,为视网膜病变诊断提供参考。     

基于广义Gamma分布的高分辨率SAR图像海岸线检测

本文针对高分辨率SAR图像,采用广义Gamma分布（GΓD）对杂波进行建模,在此基础上提出一种基于水平集分割的海岸线检测方法.GΓD是一种高度灵活的经验分布模型,能够对SAR图像不同类型的地物进行有效建模,其参数可由对数累量法估计得到.基于该分布建立能量泛函,并通过水平集方法最小化能量泛函进行海陆分割,得到海岸线检测结果.利用两幅TerraSAR-X实测SAR图像实验证明,该方法可以实现更精确的海岸线检测.     

Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density

In a less constrained capture of iris images to build a high-speed iris recognition system, the design of a robust and fast iris segmentation method is important. In this paper, a new iris segmentation technique based on the Fourier spectral density is proposed for noisy frontal view eye images captured with minimum cooperation from the subjects. The proposed segmentation method is not an iterative technique and it runs in deterministic time. The computational complexity of the proposed method is found to be significantly lower than the existing approaches based on integro-differential operator, Hough transform and active contour. The basic idea underlying the proposed method is to localize the limbic and pupil boundaries using the Fourier spectral density. The performance studies on a recently created iris database, called UBIRIS (Proenca and Alexandre in Lect Notes Comput Sci 3617:970–977, 2005) containing defocused, reflection-contained and eyelid-occluded iris images in visible spectral range, show that the proposed method is much faster than the existing methods and simultaneously achieves good segmentation accuracy.     

极化SAR图像的聚类序列投影寻踪模型方法

针对极化SAR数据的分类问题,提出了序列投影寻踪模型方法进行极化SAR数据的无监督分类.该方法的特点是利用目标散射的极化相似性参数来表征目标特征;通过遗传算法逐步给出投影寻踪方法中的最佳投影,以获取高维数据的一维投影特征;进而采用EM算法估计混合模型的参数;最后由Bayes决策准则实现分类.该文对旧金山湾地区的极化SAR数据进行分类,得到了好的分类结果,实例计算结果分别与采用强度特征的无监督分类结果和直接利用散射熵-散射角分类的结果进行了比较,说明新方法对于极化SAR数据的分类具有明显的优越性.     

基于随机森林的频谱域光学相干层析技术的图像视网膜神经纤维层分割

频谱域光学相干层析技术是一种广泛应用于眼科疾病诊断的成像技术,而视网膜层分割对青光眼的诊断有很好的参考价值。该文利用随机森林分类器寻找视网膜层间单像素宽的边界,随机森林分类器由12个特征训练产生,其中相对灰度特征和邻域特征较好地解决灰度不均匀的分割误差大问题。对10组带有青光眼病变的视网膜图像进行分割,并与传统算法和Iowa软件进行比较,平均边界绝对误差为9.202.57 m, 11.332.99 m和10.273.01 m。实验结果表明,改进算法可以较好地分割视网膜神经纤维层。     

基于CNN的康普顿背散射图像中违禁品分割方法

康普顿背散射(CBS)技术是一项较新的射线安检技术,可以提高复杂背景下安检设备对爆炸物等有机违禁品的探测力度,这其中一个重要环节就是图像中违禁品的分割问题.本文提出了一种基于细胞神经网络(CNN)的CBS图像滤波及分割方法,在此基础上又提出了一种基于CNN和数学形态学的孤立点滤除方法,并对这两种方法进行了详细分析,给出...     

改进海鸥算法的多阈值图像分割算法北大核心CSCD

为进一步提高图像分割精度,改善传统多阈值图像分割方法计算量大、分割慢的问题,提出了改进海鸥算法(improved seagull optimization algorithm,ISOA)的多阈值图像分割方案。针对原始海鸥算法(seagull optimization algorithm,SOA)存在早熟、寻优精度不足的问题,首先,采用cubic混沌映射优化初始解,提高搜索效率;其次,引入鹰栖息优化算法(eagle perching optmizer,EPO)的缩放因子和疯狂算子进行扰动,并与麻雀搜索算法(sparow search algorithm,SSA)警戒者的位置更新相结合,优化寻优精度和收敛速度,避免陷入局部最优。利用6种基准测试函数对ISOA进行寻优性能测试。最后,将ISOA与图像分割的最优阈值选取相结合,进行基于Otsu的多阈值图像分割,并与现有分割算法进行对比。仿真结果表明,ISOA在基于Otsu的图像分割中,100%取得了最优值,且80.9%的结果优于其余算法,使图像的分割精度和质量均得到了优化。     

A simple unsupervised MRF model based image segmentation approach

A simple technique has been suggested to obtain optimal segmentation based on tonal and textural characteristics of an image using the Markov random field (MRF) model. The technique takes an initially over segmented image as well as the original image as its inputs and defines an MRF over the region adjacency graph (RAG) of the initially segmented regions. A tonal-region based segmentation technique due to Kartikeyan and Sarkar (1989) has been used for initial segmentation. The energy function has been defined over the first order cliques of the MRF. The essence of this approach is primarily based on quantitative values of the second order statistics, on region characteristics and consequently deciding upon the action of merging neighboring regions using the F-statistic. The effectiveness of our approach is demonstrated with wide variety of real life examples viz., indoor, outdoor and satellite and a comparison of its output with that of a previous work in the literature has been provided.     

基于3D U-Net的轻量级脑肿瘤分割网络北大核心CSCD

针对现有脑肿瘤核磁共振成像(magnetic resonance imaging, MRI)分割神经网络的参数量和计算量较大且对肿瘤区域小目标分割精度不高的问题,提出一种改进的轻量级脑肿瘤分割网络MF-RES2Net(multiple fiber residual-like networks)。该网络以3D U-Net为基础架构,将多纤模块(multi-fiber, MF)和类残差模块(RES2)相结合代替传统卷积模块。MF将特征图像的通道进行混合,增加了通道间信息的交流融合;RES2将通道均分,单通道的卷积结果相加到相邻通道,在扩大图像感受野的同时保留了细节特征,同时降低网络参数量。此外,为改善数据不平衡问题,提出一种改进的加权损失函数,提高了网络对小目标的分割精度。将MF-RES2Net在BRATS 2019数据集进行验证,完整肿瘤、核心肿瘤和增强肿瘤分割的平均Dice系数分别为89.98%、84.02%、77.62%,参数量和浮点数分别为3.16 M和16.24 G,结果表明:该网络在降低参数量和计算量的同时进一步提升了分割性能,有效地降低了网络运行时的设备要求。     

Saliency-directed color image segmentation using modified particle swarm optimization

Color image segmentation, an ill-posed problem, can be treated as a process of dividing a color image into some constituent regions and each region is homogeneous. In this study, a saliency-directed color image segmentation approach using “simple” modified particle swarm optimization (PSO) is proposed, in which both low-level features and high-level image semantics extracted from each color image are employed. To extract high-level image semantics from each color image, the visual attention saliency map for each color image is generated by three (color, intensity, and orientation) feature maps, which is used to guide region merging using “simple” modified PSO and a hybrid fitness function for color image segmentation. The proposed approach contains four stages, namely, color quantization, feature extraction, small region elimination, and region merging using “simple” modified PSO. Based on the experimental results obtained in this study, as compared with four comparison approaches, the proposed approach usually provides the better color image segmentation results.     

基于多尺度特征提取的MRI脑肿瘤图像分割研究

针对磁共振成像(magnetic resonance imaging, MRI)脑部肿瘤区域误识别及肿瘤形状 差异较大的问题,提出一种基于多尺度特征提取的 MRI 脑肿瘤图像分割方法。分割模型以 U-Net 为骨干网络,使用密集金字塔卷积(dense pyramidal convolution, DPC)提取多尺度特征, 以适应不同尺寸肿瘤的分割,同时引入条状池化(strip pooling, SP),凭借其能捕获肿瘤中远 距离区域的依赖关系,进一步加强对肿瘤图像的分割能力。提出的方法在 Kaggle_3m 数据 集上进行了实验验证,实验结果表明该方法具有良好的脑部肿瘤分割性能, 其中Dice相似 系数、杰卡德系数分别达到了91.66%,84.38% 。     

基于Chan-Vese模型的树形结构多相水平集图像分割算法

由于Chan-Vese(C-V)模型通过单个水平集的符号将待分割图像划分为目标和背景两个部分,所以当图像的多个目标的轮廓成多连接时,C-V模型将无法表示.为了解决C-V模型在表示目标轮廓上的局限,提出了基于C-V模型的树形结构多相水平集算法.关键策略是通过改变图像背景,使得水平集在新图像上重新收敛;核心技术是依据同时明度对比提出的背景填充技术;算法流程采用多水平集串行收敛方式实现多相分割(n-1次收敛可以实现n相分割,n＞1).实验结果表明,本算法可以表示复杂的区域连接情况(n相分割最多可以表示n连接情况),能够实现多目标分割(n相分割可以实现n-1个目标分割),特别适合于目标中含有子目标的图像.     

一种基于非参数密度估计和马尔可夫上下文的SAR图像分割算法

在研究传统的基于参数的合成孔径雷达(SAR)图像统计模型基础上,为了精确估计高分辨率SAR图像的统计分布,该文提出了一种结合基于核函数的非参数估计和马尔可夫上下文的SAR图像分割算法。该算法首先采用基于核函数的非参数方法估计SAR图像的统计分布,然后将此统计量作为图像分割的似然函数,利用马尔可夫上下文约束进行SAR图像分割。该文通过软件仿真对新算法和基于参数的统计模型的算法的效果进行了比较。研究发现,基于核函数的非参数估计方法仅仅依赖实际数据,在无法准确获取分布函数解析式的情况下往往具有更好的效果。实验证明,基于核函数的非参数估计方法对高分辨率SAR图像中较为复杂的场景如城区的提取取得了更为满意的结果。