GA-based multiresolution fusion of segmented brain images using PD-, T1- and T2-weighted MR modalities

Medical image fusion has been used to derive the useful complimentary information from multimodality imaging. The proposed methodology introduces fusion approach for robust and automatic extraction of information from segmented images of different modalities. This fusion strategy is implemented in multiresolution domain using wavelet transform- and genetic algorithm-based search technique to extract maximum complementary information. The analysis of input images at multiple resolutions is able to extract more fine details and improves the quality of the composite fused image. The proposed approaches are also independent of any manual marking or knowledge of fiducial points and start the fusion procedure automatically. The performance of fusion scheme implemented on segmented brain images has been evaluated computing mutual information as similarity measuring matrix. Prior to fusion process, images are being segmented using different segmentation techniques like fuzzy C-mean and Markov random field models. Experimental results show that Gibbs- and ICM-based segmentation approaches related to Markov random field perform over the fuzzy C-mean and which are being used prior to GA-based fusion process for MR T1, MR T2 and MR PD images of section of human brain.     

基于小波图像融合算法和改进FCM聚类的MR脑部图像分割算法

针对很多基于模糊C均值(FCM)的图像分割算法存在对噪声敏感和分割轮廓不清晰等问题,提出一种基于小波变换图像融合算法和FCM聚类算法的MR医学图像分割算法。在图像分割系统的第一阶段,利用Haar小波多分辨率特性保持像素间的空间信息;第二阶段,利用小波图像融合算法对得到的多分辨率图像和原始图像进行融合,进而增强被处理图像的清晰度并降低噪声;第三阶段,利用改进型FCM技术对所处理的图像进行分割。在BrainWeb数据集上进行实验,与现有相关算法相比,提出的算法具有较高的分割精度,且对噪声的鲁棒性比较强,处理时间也没有明显增加。     

Infrared and visible image fusion using fuzzy logic and population-based optimization

This paper presents a new wavelet-based algorithm for the fusion of spatially registered infrared and visible images. Wavelet-based image fusion is the most common fusion method, which fuses the information from the source images in the wavelet transform domain according to some fusion rules. We specifically propose new fusion rules for fusion of low and high frequency wavelet coefficients of the source images in the second step of the wavelet-based image fusion algorithm. First, the source images are decomposed using dual-tree discrete wavelet transform (DT-DWT). Then, a fuzzy-based approach is used to fuse high frequency wavelet coefficients of the IR and visible images. Particularly, fuzzy logic is used to integrate the outputs of three different fusion rules (weighted averaging, selection using pixel-based decision map (PDM), and selection using region-based decision map (RDM)), based on a dissimilarity measure of the source images. The objective is to utilize the advantages of previous pixel- and region-based methods in a single scheme. The PDM is obtained based on local activity measurement in the DT-DWT domain of the source images. A new segmentation-based algorithm is also proposed to generate the RDM using the PDM. In addition, a new optimization-based approach using population-based optimization is proposed for the low frequency fusion rule instead of simple averaging. After fusing low and high frequency wavelet coefficients of the source images, the final fused image is obtained using the inverse DT-DWT. This new method provides improved subjective and objectives results as compared to previous image fusion methods.     

基于多分辨率分割的区域图像融合

图像融合是一项综合同一场景的多幅源图像信息的技术.现有的区域图像融合方法或者是只对最高层低频带分割并以此分割信息来指导所有层的融合,或者是其多分辨率分割方法过于复杂难以满足实时性.鉴于此,该文发展了一种基于多分辨率分割的区域图像融合方法.它的主要特点是多分辨率分割.其步骤为:首先对源图像进行小波变换的多分辨率分解,然后对分解后每一层的低频图像都进行区域分割,最后用每一层分割得到的区域信息来分别指导每一层的融合.仿真表明该文发展的基于多分辨率分割的区域图像融合方法的融合性能要优于传统的基于窗口的图像融合方法和只对最高层低频带分割的区域图像融合方法.     

A region-based multi-sensor image fusion scheme using pulse-coupled neural network

For most image fusion algorithms split relationship among pixels and treat them more or less independently, this paper proposes a region-based image fusion scheme using pulse-coupled neural network (PCNN), which combines aspects of feature and pixel-level fusion. The basic idea is to segment all different input images by PCNN and to use this segmentation to guide the fusion process. In order to determine PCNN parameters adaptively, this paper brings forward an adaptive segmentation algorithm based on a modified PCNN with the multi-thresholds determined by a novel water region area method. Experimental results demonstrate that the proposed fusion scheme has extensive application scope and it outperforms the multi-scale decomposition based fusion approaches, both in visual effect and objective evaluation criteria, particularly when there is movement in the objects or mis-registration of the source images.     

Pixel- and region-based image fusion with complex wavelets

A number of pixel-based image fusion algorithms (using averaging, contrast pyramids, the discrete wavelet transform and the dual-tree complex wavelet transform (DT-CWT) to perform fusion) are reviewed and compared with a novel region-based image fusion method which facilitates increased flexibility with the definition of a variety of fusion rules. A DT-CWT is used to segment the features of the input images, either jointly or separately, to produce a region map. Characteristics of each region are calculated and a region-based approach is used to fuse the images, region-by-region, in the wavelet domain. This method gives results comparable to the pixel-based fusion methods as shown using a number of metrics. Despite an increase in complexity, region-based methods have a number of advantages over pixel-based methods. These include: the ability to use more intelligent semantic fusion rules; and for regions with certain properties to be attenuated or accentuated.     

基于不完全树结构小波变换及模糊聚类神经网络的纹理分割

提出了一种新的不完全树结构小波变换用于纹理特征提取,给出了一种一人类视觉过程相一致的多分辨率多通道纹理分析方法,它由：1）特征提取：使用不完全树结构小波变换抽取纹理特征;2）基于模糊神经 网络的特征粗分类：①基于样本分布密度的模糊Kohonen聚类网络权植初始化,②使用缩减的特征向量对网络进行训练,得到粗分割结果;3）细化粗分割结果等几部分构成。实验结果证明了其有效性。     

基于小波变异粒子群和模糊熵的图像分割

基于粒子群和模糊熵的图像分割算法用于各种图像分割时,由于基本粒子群算法存在易陷入局部最优以及过早收敛的缺点,使得该算法难以得到理想的分割效果。针对此问题,提出了一种基于小波变异粒子群和模糊熵的图像分割算法,利用小波变异粒子群来搜索使模糊熵最大的参数值,得到模糊参数的最优组合,进而确定图像的分割阈值。通过与其他两种粒子群算法的分割结果进行比较,表明该算法取得了令人满意的分割结果,算法运算时间较小,具有很好的自适应性。     

An integrated approach for scene understanding based on Markov Random Field model

In this paper, we propose a Markov Random Field model-based approach as a unified and systematic way for modeling, encoding and applying scene knowledge to the image understanding problem. In our proposed scheme we formulate the image segmentation and interpretation problem as an integrated scheme and solve it through a general optimization algorithm. More specifically, the image is first segmented into a set of disjoint regions by a conventional region-based segmentation technique which operates on image pixels, and a Region Adjacency Graph (RAG) is then constructed from the resulting segmented regions based on the spatial adjacencies between regions. Our scheme then proceeds on the RAG by defining the region merging and labeling problem based on the MRF models. In the MRF model we specify the a priori knowledge about the optimal segmentation and interpretation in the form of clique functions and those clique functions are incorporated into the energy function to be minimized by a general optimization technique. In the proposed scheme, the image segmentation and interpretation processes cooperate in the simultaneous optimization process such that the erroneous segmentation and misinterpretation due to incomplete knowledge about each problem domain can be compensately recovered by continuous estimation of the single unified energy function. We exploit the proposed scheme to segment and interpret natural outdoor scene images.     

A novel local contrast fusion-based fuzzy model for color image multilevel thresholding using grasshopper optimization

The segmentation process is considered the significant step of an image processing system due to its extreme inspiration on the subsequent image analysis. Out of various approaches, thresholding is one of the most popular schemes for image segmentation. In segmentation, image pixels are arranged in various regions based on their intensity levels. In this paper, a straightforward and efficient fusion-based fuzzy model for multilevel color image segmentation using grasshopper optimization algorithm (GOA) has been proposed. Thresholding based segmentation lacks accuracy in segmenting the ambiguous images due to their complex characteristics, uncertainties and inherent fuzziness. However, the fuzzy entropy resolves these problems, but it is unable for segmenting at higher levels and also the complexity level for selecting suitable thresholds is high. The selection of metaheuristic GOA reduces this problem by selecting optimal threshold values. Therefore, to increase the quality of the segmented image, a simple and effective multilevel thresholding method is exploited by using the concept of fusion which is based on the local contrast. Experimental outputs demonstrate that fusion-based multilevel thresholding is better than most specific segmentation methods and can be validated by comparing the different numerical parameters. Experiments on standard daily-life color and satellite images are conducted to prove the effectiveness of the proposed scheme.     

HIRBIR: A hierarchical approach to region-based image retrieval

This paper proposes a hierarchical approach to region-based image retrieval (HIRBIR) based on wavelet transform whose decomposition property is similar to human visual processing. First, automated image segmentation is performed fast in the low-low (LL) frequency subband of the wavelet domain that shows the desirable low image resolution. In the proposed system, boundaries between segmented regions are deleted to improve the robustness of region-based image retrieval against segmentation-related uncertainty. Second, a region feature vector is hierarchically represented by information in all wavelet subbands, and each feature component of a feature vector is a unified color–texture feature. Such a feature vector captures well the distinctive features (e.g., semantic texture) inside one region. Finally, employing a hierarchical feature vector, the weighted distance function for region matching is tuned meaningfully and easily, and a progressive stepwise indexing mechanism with relevance feedback is performed naturally and effectively in our system. Through experimental results and comparison with other methods, the proposed HIRBIR shows a good tradeoff between retrieval effectiveness and efficiency as well as easy implementation for region-based image retrieval.     

皮肤镜黑素细胞瘤图像自适应聚类的进化寻优

图像的自动准确分割是实现黑素细胞瘤图像自动分析的关键.针对皮肤镜黑素细胞瘤图像,提出一种基于改进遗传算法和自生成神经网络(SGNN)相结合的自适应聚类分割算法.首先采用遗传算法选取一组最优的种子样本作为初始神经树;然后通过SGNN对剩余样本进行训练得到一个自生成神经森林;最后令森林中每棵树代表一个类,完成黑素细胞瘤图像的自适应聚类分割.该算法解决了SGNN对样本训练顺序敏感的问题,并能够自适应地确定类别数,聚类过程无需任何人工干预;同时根据解空间的大小设定遗传算法的初始种群规模,并在进化过程中根据个体的变化对种群规模以及交叉率和变异率等遗传控制参数进行动态调整,有效地提高了算法的运行速度.实验结果表明,文中算法稳定性好,聚类结果符合人眼判别的诊断要求.     

Object-based change detection from satellite imagery by segmentation optimization and multi-features fusion

This article presents a novel object-based change detection (OBCD) approach in high-resolution remote-sensing images by means of combining segmentation optimization and multi-features fusion. In the segmentation optimization, objects with optimized boundaries and proper sizes are generated by object intersection and merging (OIM) processes, which ensures the accurate information extraction from image objects. Within multi-features fusion and change analysis, the Dempster and Shafer (D-S) evidence theory and the Expectation-Maximization (EM) algorithm are implemented, which effectively utilize multidimensional features besides avoiding the selection of an appropriate change threshold. The main advantages of our proposed method lie in the improvement of object boundary and the fuzzy fusion of multi-features information. The proposed approach is evaluated using two different high-resolution remote-sensing data sets, and the qualitative and quantitative analyses of the results demonstrate the effectiveness of the proposed approach.     

A novel active contour model for image segmentation using local and global region-based information

In this paper, we propose a novel level set geodesic model for image segmentation. In our model, we define a hybrid signed pressure force (SPF) function integrating local and global region-based information to segment inhomogeneous images. The local region-based SPF utilizes mean values on local circular regions centered in each pixel. By introducing the local image information, the images with intensity inhomogeneity can be effectively segmented. In order to reduce the dependency on complex initialization, we incorporate a global region-based SPF into this model to develop a hybrid SPF. The global SPF and the local SPF are adaptively balanced by an adaptive weight. In addition, we also extend this model to four-phase level set formulation for brain MR image segmentation. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need for computationally expensive re-initialization. Experimental results indicate that the proposed method achieves superior segmentation performance in terms of accuracy and robustness.     

Segmentation-Driven Image Fusion Based on Alpha-Stable Modeling of Wavelet Coefficients

A novel region-based image fusion framework based on multiscale image segmentation and statistical feature extraction is proposed. A dual-tree complex wavelet transform (DT-CWT) and a statistical region merging algorithm are used to produce a region map of the source images. The input images are partitioned into meaningful regions containing salient information via symmetric alpha-stable ( ${rm S} alpha {rm S}$) distributions. The region features are then modeled using bivariate alpha-stable (${rm B} alpha {rm S}$) distributions, and the statistical measure of similarity between corresponding regions of the source images is calculated as the Kullback–Leibler distance (KLD) between the estimated ${rm B} alpha {rm S}$ models. Finally, a segmentation-driven approach is used to fuse the images, region by region, in the complex wavelet domain. A novel decision method is introduced by considering the local statistical properties within the regions, which significantly improves the reliability of the feature selection and fusion processes. Simulation results demonstrate that the bivariate alpha-stable model outperforms the univariate alpha-stable and generalized Gaussian densities by not only capturing the heavy-tailed behavior of the subband marginal distribution, but also the strong statistical dependencies between wavelet coefficients at different scales. The experiments show that our algorithm achieves better performance in comparison with previously proposed pixel and region-level fusion approaches in both subjective and objective evaluation tests.      

A general framework for multiresolution image fusion: from pixels to regions

This paper presents an overview on image fusion techniques using multiresolution decompositions. The aim is twofold: (i) to reframe the multiresolution-based fusion methodology into a common formalism and, within this framework, (ii) to develop a new region-based approach which combines aspects of both object and pixel-level fusion. To this end, we first present a general framework which encompasses most of the existing multiresolution-based fusion schemes and provides freedom to create new ones. Then, we extend this framework to allow a region-based fusion approach. The basic idea is to make a multiresolution segmentation based on all different input images and to use this segmentation to guide the fusion process. Performance assessment is also addressed and future directions and open problems are discussed as well.     

基于隶属度光滑约束的模糊C均值聚类算法

传统的FCM聚类算法未利用图像的空间信息,在分割叠加了噪声的MR图像时分割效果不理想。本文考虑到脑部MR图像真实的灰度值具有分片为常数的特性,按照合理利用图像空间信息的原则,对传统的FCM聚类算法进行了改进,增加了使隶属度趋向于分片光滑的约束项,得到了新的聚类算法。通过对模拟脑部MR图像和临床脑部MR图像的分割实验结果表明,本文提出的新算法比传统的FCM算法等多种图像分割算法有更精确的图像分割能力,并且运算简单、运算速度快、稳健性好。     

Segmentation of color images using multiscale clustering and graph theoretic region synthesis

A multiresolution color image segmentation approach is presented that incorporates the main principles of region-based segmentation and cluster-analysis approaches. The contribution of This work may be divided into two parts. In the first part, a multiscale dissimilarity measure is proposed that makes use of a feature transformation operation to measure the interregion relations with respect to their proximity to the main clusters of the image. As a part of this process, an original approach is also presented to generate a multiscale representation of the image information using nonparametric clustering. In the second part, a graph theoretic algorithm is proposed to synthesize regions and produce the final segmentation results. The latter algorithm emerged from a brief analysis of fuzzy similarity relations in the context of clustering algorithms. This analysis indicates that the segmentation methods in general may be formulated sufficiently and concisely by means of similarity relations theory. The proposed scheme produces satisfying results and its efficiency is indicated by comparing it with: 1) the single scale version of dissimilarity measure and 2) several earlier graph theoretic merging approaches proposed in the literature. Finally, the multiscale processing and region-synthesis properties validate our method for applications, such as object recognition, image retrieval, and emulation of human visual perception.     

Adaptive entropy weighted picture fuzzy clustering algorithm with spatial information for image segmentation

Image segmentation has been broadly applied in computer vision and image analysis. However, many segmentation methods suffer from limited accuracy for noisy images. To improve the robustness of the existing picture fuzzy clustering and solve the problem of selecting spatial constraint parameter, a novel picture fuzzy clustering is proposed. Firstly, a novel symmetric regularizing term is constructed to solve the time-consuming problem of existing picture fuzzy clustering, and the corresponding fuzzy clustering is proposed. Secondly, considering the correlation between current pixel and its neighboring pixels, the objective function is modified by adaptive weighting fusion of local mean information, and the maximum weight entropy constraint is embedded into it to solve the difficulty of parameter selection. Finally, the local spatial information constraint item of the current pixel is constructed by using its neighboring picture fuzzy partition information and is utilized to modify the picture fuzzy partition information of current pixel to correct the clustering center. Results show the proposed algorithm has some potential advantages in segmentation accuracy and anti-noise robustness.