基于GLCM和EM算法的纹理图像分割

基于纹理图像的特征,提出了基于灰度共生矩阵（GLCM）和快速极大似然估计（EM）算法相结合的纹理图像分割新算法,为了获得较好的纹理图像分割结果该算法采用灰度共生矩阵的三个常用特征并在四个方向上求平均,从而克服了方向的影响。采用欧式距离度量函数求得两特征向量的距离。通过用改进EM算法对距离矩阵进行聚类,得到纹理图像的初始分割结果,最后用形态学的方法实现对纹理图像边界的精确定位。     

双窗口特征的SAR图像丛林区域MRF分割算法

针对固定窗口灰度共生矩阵纹理特征对合成孔径雷达(SAR)图像丛林区域分割存在的局限性,讨论了丛林区域纹理特征值的聚类特性,分析计算窗口大小对分割的影响。基于马尔科夫随机场(MRF)分割方法对SAR图像噪声抑制能力,提出一种基于小窗口纹理特征分割作为初始标记计算初始吉布斯分布,大窗口纹理特征作为样本估计高斯分布的MRF分割方法。该方法经实验验证,能够改善分割噪声和边缘模糊的问题,很好地对SAR丛林区域进行分割。     

Boundary localization in texture segmentation

Localizing boundaries between textured image regions without sacrificing the labeling accuracy of interior regions remains a problem in segmentation. Difficulties arise because of the conflicting requirements of localization and labeling. Boundary localization usually demands observing the features over small neighborhoods, whereas labeling accuracy increases with the size of the observation neighborhood. This problem is further exacerbated in texture segmentation by the spatially distributed nature of texture features. In this correspondence, we develop a multiresolution approach that combines localized and distributed features to directly address boundary localization in texture segmentation. Maximum localization is achieved by using the gray-level discontinuities at the boundary between textures to define the boundary. The properties that characterize the gray-level discontinuity at texture boundaries are developed and an algorithm is designed to localize the boundary using these discontinuities. This segmentation algorithm is implemented and successfully tested on a set of Brodatz texture mosaics and AVHRR satellite imagery.     

基于级联Dense-UNet和图割的肝脏肿瘤自动分割

腹部CT图像肝脏肿瘤分割是进行肝脏疾病诊断、手术规划和放射治疗的重要前提。针对肝脏肿瘤灰度异质、纹理丰富、边界模糊等因素引起的分割困难,该文提出基于级联Dense-Unet和图割的自动精确鲁棒分割方法。首先运用级联的Dense-UNet获取肝脏肿瘤初始分割结果及感兴趣区域,然后利用图像像素级和区域级特征,分别构建可有效区分肿瘤与非肿瘤的灰度模型和概率模型,并将其融入图割能量函数,进一步精确分割感兴趣区域中的肿瘤组织。最后分别采用LiTS和3Dircadb公共数据库作为训练集与测试集进行实验,并与现有多种自动分割方法进行了比较。结果表明,提出方法可有效分割CT图像中灰度、形状、大小、位置各异的肝脏肿瘤,能提取更精确的肿瘤边界,尤其对于对比度低、边界模糊的肿瘤具有明显优势。     

基于小波域和活动轮廓模型的纹理图像分割

提出一种双树复小波域局部二值模式和活动轮廓模型的纹理图像分割方法.该方法首先使用双树复合小波分解纹理图像,然后使用局部二值模式提取纹理特征.利用最大熵准则对纹理特征图像进行选择,活动轮廓模型用于最后的分割.实验结果表明提出的方法对于合成纹理和自然场景数据集,达到了较高的分割精度.     

联合改进LBP和超像素级决策的高光谱图像分类

高光谱图像在有标签样本数目较少的情况下进行分类时,除了利用光谱特征外,空间纹理特征也是必不可少的。本文提出了一种利用多尺度多方向局部二值模式（LBP）描述子获取纹理特征,并结合超像素级指导决策的支持向量机分类方法。首先,本文方法将传统LBP描述子改进为多尺度多方向LBP描述子,一方面充分考虑了邻域像素之间的关系,另一方面在计算时分别考虑了水平垂直方向和对角方向。其次,在利用统计直方图获得纹理特征时,采用了多个尺寸窗口组合的方式,以获得多范围、高精度的纹理特征。第三,对传统的简单线性迭代聚类（SLIC）超像素分割方法进行改进,重新定义了光谱距离并引入了纹理特征距离,获得更精确的超像素分割图。最后,利用超像素分割图结合多数投票策略,对分类结果进行进一步的指导校正。实验表明,本文方法能够更有效的提取纹理特征,再结合超像素分割图的指导决策,进一步提升高光谱图像的分类性能。     

Segmentation of kidney from ultrasound images based on texture and shape priors

This paper presents a novel texture and shape priors based method for kidney segmentation in ultrasound (US) images. Texture features are extracted by applying a bank of Gabor filters on test images through a two-sided convolution strategy. The texture model is constructed via estimating the parameters of a set of mixtures of half-planed Gaussians using the expectation-maximization method. Through this texture model, the texture similarities of areas around the segmenting curve are measured in the inside and outside regions, respectively. We also present an iterative segmentation framework to combine the texture measures into the parametric shape model proposed by Leventon and Faugeras. Segmentation is implemented by calculating the parameters of the shape model to minimize a novel energy function. The goal of this energy function is to partition the test image into two regions, the inside one with high texture similarity and low texture variance, and the outside one with high texture variance. The effectiveness of this method is demonstrated through experimental results on both natural images and US data compared with other image segmentation methods and manual segmentation.     

基于Gabor小波的无边缘活动围道纹理分割方法

该文提出了一种基于Gabor小波的活动围道纹理分割新方法。该方法先用Gabor小波提取图像的纹理特征,再用Chan-Vese模型进行分割。与其它基于Chan-Vese模型的纹理分割方法相比,基于Gabor小波的活动围道的纹理分割方法有两个优点:一是同时使用纹理特征和灰度信息演化围道,可分割纹理图像和非纹理图像,分割方法的灵活性好;二是在分割多类目标时,采用多级分层式曲线演化方法解决了初始围道难以选择的问题。对自然界真实图像和遥感图像的分割实验结果说明,该文提出的分割方法精度高。     

Vegetation segmentation based on variational level set using multi-channel local wavelet texture and color

The existing spectrum index-based methods for detecting vegetation coverage suffer from an over-dependence on spectrum. To address these issues, this paper proposes a graph cut-based variational level set segmentation algorithm that combines multi-channel local wavelet texture (MCLWT) and color. First, the prior color is generated by automatic estimation based on the mathematical morphology with a color histogram. Then, local wavelet texture features are extracted using a multi-scale and orientation Gabor wavelet transformation followed by local median and entropy filtering. Next, in addition to the energy of color, that of MCLWT is integrated into the variational level set model based on kernel density estimation. Consequently, all energies are integrated into the graph cut-based variational level set model. Finally, the proposed energy functional is made convex to obtain a global optimal solution, and a primal-dual algorithm with global relabeling is adopted to accelerate the evolution of the level sets. A comparison of the segmentation results from our proposed algorithm and other state-of-the-art algorithms showed that our algorithm effectively reduces the over-dependence on color and yields more accurate results in detecting vegetation coverage.     

基于Word Embedding的遥感影像检测分割

遥感影像检测分割技术通常需提取影像特征并通过深度学习算法挖掘影像的深层特征来实现.然而传统特征（如颜色特征、纹理特征、空间关系特征等）不能充分描述影像语义信息,而单一结构或串联算法无法充分挖掘影像的深层特征和上下文语义信息.针对上述问题,本文通过词嵌入将空间关系特征映射成实数密集向量,与颜色、纹理特征的结合.其次,本文构建基于注意力机制下图卷积网络和独立循环神经网络的遥感影像检测分割并联算法（Attention Graph Convolution Networks and Independently Recurrent Neural Network,ATGIR）.该算法首先通过注意力机制对结合后的特征进行概率权重分配;然后利用图卷积网络（GCNs）算法对高权重的特征进一步挖掘并生成方向标签,同时使用独立循环神经网络（IndRNN）算法挖掘影像特征中的上下文信息,最后用Sigmoid分类器完成影像检测分割任务.以胡杨林遥感影像检测分割任务为例,我们验证了提出的特征提取方法和ATGIR算法能有效提升胡杨林检测分割任务的性能.     

Multipolarization SAR Data for Surface Feature Delineation and Forest Vegetation Characterization

This paper presents the utility of multipolarization Synthetic Aperture Radar (SAR) data for surface feature delineation and forest vegetation characterization. Three channels of ratioed data (VV/HH, VH/HH, and VH/VV) are generated from the HH, VV, and VH polarization data (V = vertical, H = horizontal). The ratioed data are linearly stretched to yield a digital number within a range of 0 to 255. The techniques for reducing SAR speckle noise and for measuring the degree of separation are discussed. For surface feature delineation, the results indicate that cross polarization as well as cross polarization ratioed data better delineate those surface features that are difficult to separate by like polarization data. The results suggest using a median value filtering technique to reduce within-plot data fluctuation to increase the separability measure. For forest vegetation characterization, the results indicate that multipolarization SAR data may be used to estimate forest properties such as total-tree biomass, basal area, and tree height.     

A methodology for evaluation of boundary detection algorithms onmedical images

Image segmentation is the partition of an image into a set of nonoverlapping regions whose union is the entire image. The image is decomposed into meaningful parts which are uniform with respect to certain characteristics, such as gray level or texture. In this paper, we propose a methodology for evaluating medical image segmentation algorithms wherein the only information available is boundaries outlined by multiple expert observers. In this case, the results of the segmentation algorithm can be evaluated against the multiple observers' outlines. We have derived statistics to enable us to find whether the computer-generated boundaries agree with the observers' hand-outlined boundaries as much as the different observers agree with each other. We illustrate the use of this methodology by evaluating image segmentation algorithms on two different applications in ultrasound imaging. In the first application, we attempt to find the epicardial and endocardial boundaries from cardiac ultrasound images, and in the second application, our goal is to find the fetal skull and abdomen boundaries from prenatal ultrasound images     

A simplified texture gradient method for improved image segmentation

Inspired by the probability of boundary (Pb) algorithm, a simplified texture gradient method has been developed to locate texture boundaries within grayscale images. Despite considerable simplification, the proposed algorithm’s ability to locate texture boundaries is comparable with Pb’s texture boundary method. The proposed texture gradient method is also integrated with a biologically inspired model, to enable boundaries defined by discontinuities in both intensity and texture to be located. The combined algorithm outperforms the current state-of-art image segmentation method (Pb) when this method is also restricted to using only local cues of intensity and texture at a single scale.     

Learning-based segmentation framework for tissue images containing gene expression data

Associating specific gene activity with functional locations in the brain results in a greater understanding of the role of the gene. To perform such an association for the more than 20 000 genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this paper, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quantified and compared with other images. Our contribution is a novel hybrid atlas that utilizes a statistical shape model based on a subdivision mesh, texture differentiation at region boundaries, and features of anatomical landmarks to delineate boundaries of anatomical regions in gene expression images. This atlas, which provides a common coordinate system for internal brain data, is being used to create a searchable database of gene expression patterns in the adult mouse brain. Our framework annotates the images about four times faster and has achieved a median spatial overlap of up to 0.92 compared with expert segmentation in 64 images tested. This tool is intended to help scientists interpret large-scale gene expression patterns more efficiently.     

基于Gabor小波和主元分析的纹理图像分割

提出了基于Gabor小波和主元分析相结合的纹理图像分割算法.首先对纹理图像进行多通道滤波,获得了一系列滤波后的纹理图像.其次,借助于“能量测度”的概念,求解出各象素有效的纹理特征.为了进一步减少特征之间的信息冗余,降低聚类分析的计算负荷,采用主元分析（PCA）对所得的纹理特征进行降维.然后利用K-Mean算法实现纹理图像的分类.最后针对所提算法,进行了仿真试验.     

Texture boundary detection using network of synchronised oscillators

A new segmentation method, based on detection of texture boundaries using a network of synchronised oscillators, is proposed. The method was tested on magnetic resonance images of human foot cross-section. The results obtained are discussed and compared with the multilayer perceptron network image segmentation technique.     

Towards multi-stage texture-based active contour image segmentation

In this paper, we present a three-stage approach to incorporation of texture analysis into a two-dimensional active contour segmentation framework. This approach allows to utilise texture information alongside other image features. The proposed method starts with an initial unsupervised feature computation and selection, then moves to a fast contour evolution process and ends with a final refinement stage. The algorithm is designed to be general in its nature and not restricted to any particular texture feature extraction method. In this paper, the initial stage generates a set of feature maps consisting of grey-level co-occurrence matrix and Gabor features. The implementation makes an extensive use of hardware acceleration for efficient calculation of a relatively large number of features. The performance of the method was tested on various synthetic and natural images and compared with results of other algorithms.     

Image and texture segmentation using local spectral histograms.

We present a method for segmenting images consisting of texture and nontexture regions based on local spectral histograms. Defined as a vector consisting of marginal distributions of chosen filter responses, local spectral histograms provide a feature statistic for both types of regions. Using local spectral histograms of homogeneous regions, we decompose the segmentation process into three stages. The first is the initial classification stage, where probability models for homogeneous texture and nontexture regions are derived and an initial segmentation result is obtained by classifying local windows. In the second stage, we give an algorithm that iteratively updates the segmentation using the derived probability models. The third is the boundary localization stage, where region boundaries are localized by building refined probability models that are sensitive to spatial patterns in segmented regions. We present segmentation results on texture as well as nontexture images. Our comparison with other methods shows that the proposed method produces more accurate segmentation results.     

Estimating Gaussian Markov random field parameters in anonstationary framework: application to remote sensing imaging

In this paper, we tackle the problem of estimating textural parameters. We do not consider the problem of texture synthesis, but the problem of extracting textural features for tasks such as image segmentation. We take into account nonstationarities occurring in the local mean. We focus on Gaussian Markov random fields for which two estimation methods are proposed, and applied in a nonstationary framework. The first one consists of extracting conditional probabilities and performing a least square approximation. This method is applied to a nonstationary framework, dealing with the piecewise constant local mean. This framework is adapted to practical tasks when discriminating several textures on a single image. The blurring effect affecting edges between two different textures is thus reduced. The second proposed method is based on renormalization theory. Statistics involved only concern variances of Gaussian laws, leading to Cramer-Rao estimators. This method is thus especially robust with respect to the size of sampling. Moreover, nonstationarities of the local mean do not affect results. We then demonstrate that the estimated parameters allow texture discrimination for remote sensing data. The first proposed estimation method is applied to extract urban areas from SPOT images. Since discontinuities of the local mean are taken into account, we obtain an accurate urban areas delineation. Finally, we apply the renormalization based on method to segment ice in polar regions from AVHRR data.