基于模糊C均值聚类的多分量彩色图像分割算法

以模糊C均值(FCM)聚类理论为基础,选用符合人眼视觉特性的HSI颜色空间,提出了一种新的多分量彩色图像分割算法。该算法首先结合数据分布特点确定出H分量与I分量的初始聚类中心;然后利用FCM聚类技术对H分量、I分量进行分类处理,以得到不同分量的像素点隶属度;最后,将所得到的不同分量像素点隶属度组织成2维特征,并以此进行模糊聚类图像分割。实验结果表明,该算法可有效提高图像分割效果,其分割结果优于传统FCM聚类图像分割方案。     

Supervised image segmentation using Q-Shift Dual-Tree Complex Wavelet Transform coefficients with a texton approach

In this study, we propose a simple and efficient texture-based algorithm for image segmentation. This method constitutes computing textons and bag of words (BOWs) learned by support vector machine (SVM) classifiers. Textons are composed of local magnitude coefficients that arise from the Q-Shift Dual-Tree Complex Wavelet Transform (DT-CWT) combined with color components. In keeping with the needs of our research context, which addresses land cover mapping from remote images, we use a few small texture patches at the training stage, where other supervised methods usually train fully representative textures. We accounted for the scale and rotation invariance issue of the textons, and three different invariance transforms were evaluated on DT-CWT-based features. The largest contribution of this study is the comparison of three classification schemes in the segmentation algorithm. Specifically, we designed a new scheme that was especially competitive and that uses several classifiers, with each classifier adapted to a specific size of analysis window in texton quantification and trained on a reduced data set by random selection. This configuration allows quick SVM convergence and an easy parallelization of the SVM-bank while maintaining a high segmentation accuracy. We compare classification results with textons made using the well-known maximum response filters bank and speed up robust features features as references. We show that DT-CWT textons provide better distinguishing features in the entire set of configurations tested. Benchmarks of our different method configurations were made over two substantial textured mosaic sets, each composed of 100 grey or color mosaics made up of Brodatz or VisTex textures. Lastly, when applied to remote sensing images, our method yields good region segmentation compared to the ENVI commercial software, which demonstrates that the method could be used to generate land cover maps and is suitable for various purposes in image segmentation.     

A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

To overcome the shortcomings of 1D and 2D Otsu’s thresholding techniques, the 3D Otsu method has been developed. Among all Otsu’s methods, 3D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image; it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional 1D Otsu, 2D Otsu and 3D Otsu methods, as evident from the objective and subjective evaluations.      

一种快速、稳健的图像分割方法

提出了一种快速、稳健的彩色图像分割算法。首先将图像划分为多个色彩变化平滑区域和非平滑区域,进而对非平滑区域进行进一步量化,最后使用区域增长法合并较小区域从而实现图像分割。试验结果表明,该算法可以获得良好的图像分割结果并具有较高的效率。     

Multi-feature sea–land segmentation based on pixel-wise learning for optical remote-sensing imagery

Robust sea–land segmentation in optical remote-sensing images is challenging because of the complex sea–land environment and scene diversity. Here, we propose a novel multi-feature sea–land segmentation method via pixel-wise learning for optical remote-sensing images. Multiple features such as greyscale, local statistical information, edge, texture, and structure are first extracted from each pixel in training images and then used to learn a multi-feature sea–land classifier, which transforms the segmentation issue into pixel-wise binary classification problem. In our approach, a new multi-feature sea–land segmentation algorithm is put forward based on the approximation of Newton method. Experiments on Google-Earth, Venezuelan Remote Sensing Satellite-1 (VRSS-1) and Gaofen-1 images demonstrate that the proposed approach yields more robust and accurate sea–land segmentation results.     

Scale‐invariant segmentation of dynamic contrast‐enhanced perfusion MR images with inherent scale selection

Selection of the best set of scales is problematic when developing signal‐driven approaches for pixel‐based image segmentation. Often, different possibly conflicting criteria need to be fulfilled in order to obtain the best trade‐off between uncertainty (variance) and location accuracy. The optimal set of scales depends on several factors: the noise level present in the image material, the prior distribution of the different types of segments, the class‐conditional distributions associated with each type of segment as well as the actual size of the (connected) segments. We analyse, theoretically and through experiments, the possibility of using the overall and class‐conditional error rates as criteria for selecting the optimal sampling of the linear and morphological scale spaces. It is shown that the overall error rate is optimized by taking the prior class distribution in the image material into account. However, a uniform (ignorant) prior distribution ensures constant class‐conditional error rates. Consequently, we advocate for a uniform prior class distribution when an uncommitted, scale‐invariant segmentation approach is desired. Experiments with a neural net classifier developed for segmentation of dynamic magnetic resonance (MR) images, acquired with a paramagnetic tracer, support the theoretical results. Furthermore, the experiments show that the addition of spatial features to the classifier, extracted from the linear or morphological scale spaces, improves the segmentation result compared to a signal‐driven approach based solely on the dynamic MR signal. The segmentation results obtained from the two types of features are compared using two novel quality measures that characterize spatial properties of labelled images. Copyright © 2002 John Wiley & Sons, Ltd.     

Segmentation-based multi-class semantic object detection

In this paper we study the problem of the detection of semantic objects from known categories in images. Unlike existing techniques which operate at the pixel or at a patch level for recognition, we propose to rely on the categorization of image segments. Recent work has highlighted that image segments provide a sound support for visual object class recognition. In this work, we use image segments as primitives to extract robust features and train detection models for a predefined set of categories. Several segmentation algorithms are benchmarked and their performances for segment recognition are compared. We then propose two methods for enhancing the segments classification, one based on the fusion of the classification results obtained with the different segmentations, the other one based on the optimization of the global labelling by correcting local ambiguities between neighbor segments. We use as a benchmark the Microsoft MSRC-21 image database and show that our method competes with the current state-of-the-art.     

支持向量机在核磁共振脑组织图像分割中的应用

在医学图像分析中,脑组织图像分割有着重要的研究与应用价值。采用支持向量机方法对核磁共振脑图像进行研究。传统的支持向量机方法在图像分割中一般选用方形的区域,用该区域的像素灰度和纹理特征作为支持向量的训练样本,对图像进行提取和分析,得到分类结果。提出了一种新型的研究区域,在该区域上提取训练样本,对核磁共振脑图像进行分类。分类结果显示用新型区域做的图像分割提高了正确率。     

A system for 3D texture-based probabilistic object recognition and its applications

This article presents a system for texture-based probabilistic classification and localisation of three-dimensional objects in two-dimensional digital images and discusses selected applications. In contrast to shape-based approaches, our texture-based method does not rely on object features extracted using image segmentation techniques. Rather, the objects are described by local feature vectors computed directly from image pixel values using the wavelet transform. Both gray level and colour images can be processed. In the training phase, object features are statistically modelled as normal density functions. In the recognition phase, the system classifies and localises objects in scenes with real heterogeneous backgrounds. Feature vectors are calculated and a maximisation algorithm compares the learned density functions with the extracted feature vectors and yields the classes and poses of objects found in the scene. Experiments carried out on a real dataset of over 40,000 images demonstrate the robustness of the system in terms of classification and localisation accuracy. Finally, two important real application scenarios are discussed, namely recognising museum exhibits from visitors’ own photographs and classification of metallography images.     

Color image segmentation using automatic pixel classification with support vector machine

Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM model (classifier). Then, the SVM model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.     

Image Segmentation Using Markov Random Field Model in Fully Parallel Cellular Network Architectures

Markovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. Herein, we show that the Markovian labeling approach can be implemented in fully parallel cellular network architectures, using simple functions and data representations. This makes possible to implement our model in parallel imaging VLSI chips.As an example, we have developed a simplified statistical image segmentation algorithm for the Cellular Neural/Nonlinear Networks Universal Machine (CNN-UM), which is a new image processing tool, containing thousands of cells with analog dynamics, local memories and processing units. The Modified Metropolis Dynamics (MMD) optimization method can be implemented into the raw analog architecture of the CNN-UM. We can introduce the whole pseudo-stochastic segmentation process in the CNN architecture using 8 memories/cell. We use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (step, jigsaw). With this architecture, the proposed VLSI CNN chip can execute a pseudo-stochastic relaxation algorithm of about 100 iterations in about 100 μs.In the suggested solution the segmentation is unsupervised, where a pixel-level statistical estimation model is used. We have tested different monogrid and multigrid architectures.In our CNN-UM model several complex preprocessing steps can be involved, such as texture-classification or anisotropic diffusion. With these preprocessing steps, our fully parallel cellular system may work as a high-level image segmentation machine, using only simple functions based on the close-neighborhood of a pixel.     

融合分割先验的多图像目标语义分割

目的 在序列图像或多视角图像的目标分割中，传统的协同分割算法对复杂的多图像分割鲁棒性不强，而现有的深度学习算法在前景和背景存在较大歧义时容易导致目标分割错误和分割不一致。为此，提出一种基于深度特征的融合分割先验的多图像分割算法。方法 首先，为了使模型更好地学习复杂场景下多视角图像的细节特征，通过融合浅层网络高分辨率的细节特征来改进PSPNet-50网络模型，减小随着网络的加深导致空间信息的丢失对分割边缘细节的影响。然后通过交互分割算法获取一至两幅图像的分割先验，将少量分割先验融合到新的模型中，通过网络的再学习来解决前景/背景的分割歧义以及多图像的分割一致性。最后通过构建全连接条件随机场模型，将深度卷积神经网络的识别能力和全连接条件随机场优化的定位精度耦合在一起，更好地处理边界定位问题。结果 本文采用公共数据集的多图像集进行了分割测试。实验结果表明本文算法不但可以更好地分割出经过大量数据预训练过的目标类，而且对于没有预训练过的目标类，也能有效避免歧义的区域分割。本文算法不论是对前景与背景区别明显的较简单图像集，还是对前景与背景颜色相似的较复杂图像集，平均像素准确度（PA）和交并比（IOU）均大于95%。结论 本文算法对各种场景的多图像分割都具有较强的鲁棒性，同时通过融入少量先验，使模型更有效地区分目标与背景，获得了分割目标的一致性。     

局部自适应的灰度图像彩色化

目的 现有的灰度图像彩色化方法为了保证彩色化结果在颜色空间上的一致性,往往采用全局优化的算法,使得图像边界区域易产生过渡平滑现象。为此提出一种局部自适应的灰度图像彩色化方法,在迁移过程中考虑局部邻域像素信息,同时自动调节邻域像素权重,在颜色正确迁移的同时保证清晰的边界信息。方法 首先结合SVM（support vector machine）和ISLIC（improved simple linear iterative clustering）算法获取彩色图像和灰度图像分类结果图;然后在分类基础上,确定灰度图像高置信度像素点,并根据图像纹理特征,在彩色图像中寻找灰度图像的像素匹配点;最后利用自适应权重均值滤波实现高置信度匹配像素点的颜色迁移,并利用迁移结果对低置信度像素点进行颜色扩散,以完成灰度图像彩色化。结果 实验结果显示,本文方法获得的彩色化迁移结果评分均高于3.5分,特别是局部放大区域评价结果均接近或高于4.0分,高于其他现有彩色化方法评价分数。表明本文方法不仅能够保证颜色迁移的准确性和颜色空间的一致性,同时也能获取颜色区分度高的边界细节信息。与现有的典型灰度图像彩色化方法相比,彩色化结果图在颜色迁移的正确性和抑制边界区域颜色的过渡平滑上都有更优的表现。结论 本文算法为灰度图像彩色化过程中抑制颜色越界问题提供了新的指导方法,能有效地应用于遥感、黑白图像/视频处理、医学图像着色等领域。     

Multispectral image segmentation by a multichannel watershed‐based approach

Watershed transformation in mathematical morphology is a powerful morphological tool for image segmentation that is usually defined for greyscale images and applied to the gradient magnitude of an image. This paper presents an extension of the watershed algorithm for multispectral image segmentation. A vector‐based morphological approach is proposed to compute gradient magnitude from multispectral imagery, which is then input into watershed transformation for image segmentation. The gradient magnitude is obtained at multiple scales. After an automatic elimination of local irrelevant minima, a watershed transformation is applied to segment the image. The segmentation results were evaluated and compared with other multispectral image segmentation methods, in terms of visual inspection, and object‐based image classification using high resolution multispectral images. The experimental results indicate that the proposed method can produce accurate segmentation results and higher classification accuracy, if the scales and contrast parameter are appropriately selected in the gradient computation and subsequent local minima elimination. The proposed method shows encouraging results and can be used for segmentation of high resolution multispectral imagery and object based classification.     

结合分段频域和局部注意力的超声甲状腺分割

目的 超声检查是诊断甲状腺疾病的主要影像学方法之一,但由于超声图像中斑点强度具有随机性、组织器官复杂等问题,导致甲状腺在不同数据源间的形态、大小和纹理差异性较大,容易导致观察者视觉疲劳。针对甲状腺超声成像存在斑点强度随机性以及周边组织复杂性的问题,为了更准确地描述出器官与病理性病变的解剖边界,提出一种基于频域增强和局部注意力机制的甲状腺超声分割网络。方法 针对原始数据采用高低通滤波器获取高低频段的图像信息,整合高频段细节特征与低频段边缘特征,增强图像前背景的对比度,降低图像间的差异性。根据卷积网络中网络深度所提取特征信息量的不同,采用局部注意力机制对高低维特征信息进行自适应激活,增强低维特征的细节信息,弱化对非目标区域的关注,增强高维特征的全局信息,弱化冗余信息对网络的干扰,增强前背景分类以及对非显著性目标检测的能力。采用金字塔级联空洞卷积获取不同感受野的特征信息,解决数据源间图像差异较大的问题。结果 实验结果表明,本文方法在11~16 MHz时采集的16个手绘甲状腺超声公开数据集中,通过10折交叉验证显示准确率为0.989,召回率为0.849,精准率为0.940,Dice系数为0.812,效果优于当前其他医学图像分割网络。通过消融实验,证明本文的几个模块对超声图像分割确实具有一定的提升效果。结论 本文所提分割网络,结合深度学习模型及传统图像处理模型的优点,能较好地处理超声图像随机斑点并且提升非显著性组织分割效果。     

WND-CHARM: Multi-purpose image classification using compound image transforms

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier's high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from openmicroscopy.org.     

基于热平衡的中智学图像分割方法

为降低噪声对图像分割结果的影响,提出一种基于热平衡理论的中智学图像分割方法。该方法将图像转化为中智学图像,考虑每一个像素的不确定性,通过热平衡运算及图像增强处理,使噪声点变得更平滑,再使用γ-均值聚类方法分割图像。实验结果表明,对于含不同程度噪声的图像,该方法的分割效果明显优于中智学方法及改进的模糊C-均值方法。     

结合纹理与形状的Tagged MR图像左心室分割算法

分割带标记线核磁共振(tagged MR)图像是左心室运动重建的前提.由于标记线的加载破坏了左心室的轮廓边缘和区域灰度一致性,再加上乳突肌的存在,使带标记线核磁共振图像的左心室内外轮廓分割变得相当困难.在变分框架下,将纹理分类信息与形状统计先验知识引入Mumford-Shah模型中,提出了一种改进的分割带标记线核磁共振图像的左心室内外轮廓的方法.该方法基于支持向量机对S滤波器组提取的纹理特征的分类结果,构造了一种新的图像能量表示;针对乳突肌及边缘断裂现象,引入形状统计先验信息来约束曲线的演化.因为分割过程利用了有监督学习策略,较好地克服了标记线对左心室区域灰度的影响,提高了分割精度.实验结果表明,该方法较以往方法具有更高的分割精度和更好的稳定性.     

Combining belief networks and neural networks for scenesegmentation

We are concerned with the problem of image segmentation, in which each pixel is assigned to one of a predefined finite number of labels. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of label images. Following the work of Bouman and Shapiro (1994), we consider the use of tree-structured belief networks (TSBNs) as prior models. The parameters in the TSBN are trained using a maximum-likelihood objective function with the EM algorithm and the resulting model is evaluated by calculating how efficiently it codes label images. A number of authors have used Gaussian mixture models to connect the label field to the image data. We compare this approach to the scaled-likelihood method of Smyth (1994) and Morgan and Bourlard (1995), where local predictions of pixel classification from neural networks are fused with the TSBN prior. Our results show a higher performance is obtained with the neural networks. We evaluate the classification results obtained and emphasize not only the maximum a posteriori segmentation, but also the uncertainty, as evidenced e.g., by the pixelwise posterior marginal entropies. We also investigate the use of conditional maximum-likelihood training for the TSBN and find that this gives rise to improved classification performance over the ML-trained TSBN