SAR图像中河流边缘检测的Wavelet snake算法

图像的边缘检测对图像的分割、图像信息的提取等都非常重要。由于闪烁光斑的原因,SAR图像的边缘检测比一般的光学图像更难。利用àtrous小波变换、图像块生长和wavelet snake算法相结合,本文提出了一种检测SAR图像中河岸边缘的新算法,并成功用于提取淮河SAR图像中的一段水岸边缘。     

基于双正交小波变换的资源二号卫星与多光谱影像融合方法研究

针对基于Darbechies等正交小波函数的遥感影像融合时引起失真的现象,引入双正交小波函数,提出了基于双正交小波变换的融合方法,对资源二号卫星和TM的多光谱影像的融合进行研究,并把融合结果与主成分、IHS和Brovey等融合结果进行比较。通过对光谱特征曲线、相关系数、光谱扭曲程度等分析和空间细节的对比,说明双正交小波融合方法除了提高空间分辨率外,最好地保持了多光谱影像的光谱特征,有利于专题信息的提取。     

Airplane extraction and identification by improved PCNN with wavelet transform and modified Zernike moments

At an airport, the information of the number and positions of airplanes is very important for the applications of air navigation. Especially, the information from airplane extraction and identification is significant in both civil and military remote sensing. In this paper, according to the characteristics of airplanes and airport in satellite remote sensing images, a new airplane image segmentation algorithm is proposed based on improved pulse-coupled neural network (PCNN) with wavelet transform, and airplane identification algorithm is carried out by using modified Zernike moments. Firstly, for an original image, a PCNN model is improved and then used to do image segmentation by combining the wavelet transform. Then, in order to reduce the number of irrespective targets in the image and increase the processing speed, the airplanes in the original image are roughly detected on the characteristics of the segmented object contour geometries. Finally, the Zernike moments are modified and then applied to identify the roughly detected airplanes accurately. By comparing to the five traditional image segmentation algorithms for the same airplane images, the testing results show that the improved PCNN image segmentation algorithm can segment and detect airplane regions at an airport accurately at a high recognising rate and with high recognising stability, and it is not affected by the image shadows and rotations.     

Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis

Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X-ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta-Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.     

Multi-resolution fuzzy clustering approach for image-based particle characterization for particle systems

Online characterization of particles is an important step for maintaining desired product quality in particulate processes. Direct real-time image analysis is a promising method for monitoring particle systems, and is becoming increasingly more attractive due to availability of high speed imaging devices and equally powerful computers. Performing image segmentation (separation of objects (particles) within one image) accurately becomes a key issue in particle image analysis. This paper presents a novel technique based on combining wavelet transform and Fuzzy C-means Clustering (FCM) for particle image segmentation. Through performing wavelet transform on images, the noise and high frequency components of images can be eliminated and the textures and features can be obtained. FCM is then used to divide data into two clusters to separate touching objects. To quantitatively evaluate this method, a case study involving a particle image is investigated. The procedure of selecting optimum wavelet function and decomposition level for this image is presented. ‘Fuzzy range’ is used as a derived feature for segmentation. The number of particles, particle equivalent diameters, and size distribution before and after partition are discussed. The results show that this method is effective and reliable.     

基于区域MRF的SAR图像快速分割算法

针对受相干斑噪声影响较严重的合成孔径雷达(SAR)图像,提出了一种基于边缘保持(EPR)的区域MRF快速分割算法.基于EPR的SAR图像表示方法包括各向异性扩散的相干斑降噪算法和分水岭变换两部分,该方法在存在相干斑噪声的情况下,能够有效地抑制过分割和在区域边界进行目标边缘的准确定位.将基于EPR的表示方法和区域MRF相结合,能够大幅减少优化过程的搜索空间,获得准确的分类结果和统计特性,同时减少了计算量和分割错误.将提出的算法用于一幅添加了各种不同噪声水平的合成图像和SAR海冰影像的分割中,实验结果证明了该算法的有效性.该算法与现有的区域MRF相比,实验结果证明新算法能够节约计算时间50%,同时提高了分割准确性,尤其是在相干斑噪声较强的区域.     

基于 NSCT 域特征和 PCNN 的SAR 图像目标分割

针对 SAR 图像的目标自动分割问题,在分析非下采样轮廓波变换和脉冲耦合神经网络的基础上,提出了一种基于非下采样轮廓波域特征图和 PCNN 的 SAR 图像目标分割算法.对 SAR 图像经过 NSCT 分解后的高、低频图像分别运用不同方式进行处理.对低频图用 PCNN 进行分割以获取目标所在的区域,对高频子带构造了特征图,对特征图利用 PCNN 进行分割以获取目标的精细结构.利用 MSTAR 数据进行了仿真实验,并与基于模糊 C 均值的分割算法、基于马尔可夫随机场的分割算法进行了对比.实验结果表明,所提出算法对 SAR 图像目标的分割结果更为准确,同时较其它算法具有更强的抗噪性能,是一种有效可行的 SAR 目标分割算法.     

Texture Feature Extraction Method for Ground Nephogram Based on Contourlet and the Power Spectrum Analysis Algorithm

It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection. In this paper, a new method is presented to capture the contour edge, texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm. More abundant texture information is extracted. Cloud images can be obtained a multiscale and multidirection decomposition. The coefficient matrix from Contourlet transform of ground nephogram is calculated. The energy, mean and variance characteristics calculated from coefficient matrix are composed of the feature information. The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis. Then Support Vector Machines (SVM) classifier is used to classify according to the frequency information of the trend graph of data series. It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.     

Steganography Using Reversible Texture Synthesis Based on Seeded Region Growing and LSB

Steganography technology has been widely used in data transmission with secret information. However, the existing steganography has the disadvantages of low hidden information capacity, poor visual effect of cover images, and is hard to guarantee security. To solve these problems, steganography using reversible texture synthesis based on seeded region growing and LSB is proposed. Secret information is embedded in the process of synthesizing texture image from the existing natural texture. Firstly, we refine the visual effect. Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture. We use seeded region growing algorithm to ensure texture’s similar local appearance. Secondly, the size and capacity of image can be decreased by introducing the information segmentation, because the capacity of the secret information is proportional to the size of the synthetic texture. Thirdly, enhanced security is also a contribution in this research, because our method does not need to transmit parameters for secret information extraction. LSB is used to embed these parameters in the synthetic texture.     

小波多辨率CT成像及处理算法

分析了小波变换进行低分辨率快速图像轮廓重构和局部区域精确重构的算法。在这种算法中，滤波器与小波有关，从而可由反投影得到各种小波图像。通过小波多尺度分析和小波系数控制，提出一种简单算法进行图像增强和噪声去除。与标准的算法相比，该算法提高了重建速度和图像精度。     

一种改进多分辨率图像融合算法

提出一种基于局部熵的多分辨图像融合算法。利用小波变换得到待融合图像的多分辨结构,同时得到图像的多分辨局部熵序列。以局部熵为判据,在图像多分辨结构相应各级上进行融合,得到融合图像的多分辨结构,利用小波逆变换重构融合图像。实验结果表明,该图像融合方法在保留TM多光谱图像光谱分辨率的同时,通过融合SPOT全色图像提高了空间分辨率,丰富了图像细节信息。     

Integration of synthetic aperture radar image segmentation method using Markov random field on region adjacency graph

A novel approach to obtain precise segmentation of synthetic aperture radar (SAR) images using Markov random field model on region adjacency graph (MRF-RAG) is presented. First, to form a RAG, the watershed algorithm is employed to obtain an initially over-segmented image. Then, a novel MRF is defined over the RAG instead of pixels so that the erroneous segmentation caused by speckle in SAR images can be avoided and the number of configurations for the combinatorial optimisation can be reduced. Finally, a modification method based on Gibbs sampler is proposed to correct edge errors, brought by the over-segmented algorithm, in the segmentations obtained by MRF-RAG. The experimental results both on simulated and real SAR images show that the proposed method can reduce the computational complexity greatly as well as increase the segmentation precision.     

基于小波系数邻域特征的CT与MR图像融合方法

提出一种基于小波变换的像素级CT,MR医学图像融合方法,利用离散小波变换分别将两幅源图像进行多尺度分解,再用不同的小波系数邻域特征指导高频分量和低频分量的小波系数的融合,低频分量采用邻域方差指导,高频分量采用邻域能量指导,最后根据融合图像的各小波系数重构融合图像.实验表明:不论从主观感受,还是采用信息熵和平均梯度两项指标作为客观定量评价标准,该方法都优于传统的融合方法,获得的融合图像有效地综合了CT与MR图像信息,能够同时清晰地显示脑部骨组织和软组织.     

一种基于Directionlet变换的图像融合算法

为了提高图像融合效果,提出了一种基于Directionlet变换的图像融合算法.首先对已配准的待融合源图像由给定的生成矩阵分别进行陪集分解,得到每个陪集对应的子图;接着将每两个子图相减,得到源图像的高频和低频分量,其中边缘、纹理等奇异特征包含在高频分量中;然后对低频分量采用直接平均融合的方法进行系数选择,对高频分量选择子区域边缘信息较强的系数;最后,通过Directionlet陪集分解的反变换,得到融合后的图像.多聚焦图像融合实验表明,在主观视觉上,该算法明显更好地融合了边缘等图像特征,从而较好地保持了左右聚焦图像各自的细节信息;在客观评价上,通过熵、平均梯度、标准差和互信息量等性能参数比较,该方法也优于小波变换和其他的融合方法.     

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.     

Kernel-based spectral color image segmentation

In this work, we propose a new algorithm for spectral color image segmentation based on the use of a kernel matrix. A cost function for spectral kernel clustering is introduced to measure the correlation between clusters. An efficient multiscale method is presented for accelerating spectral color image segmentation. The multiscale strategy uses the lattice geometry of images to construct an image pyramid whose hierarchy provides a framework for rapidly estimating eigenvectors of normalized kernel matrices. To prevent the boundaries from deteriorating, the image size on the top level of the pyramid is generally required to be around 75 x 75, where the eigenvectors of normalized kernel matrices would be approximately solved by the Nystr?m method. Within this hierarchical structure, the coarse solution is increasingly propagated to finer levels and is refined using subspace iteration. In addition, to make full use of the abundant color information contained in spectral color images, we propose using spectrum extension to incorporate the geometric features of spectra into similarity measures. Experimental results have shown that the proposed method can perform significantly well in spectral color image segmentation as well as speed up the approximation of the eigenvectors of normalized kernel matrices.     

基于小波变换的图像融合方法研究

图像融合是多传感器信息融合的一个重要分支,小波变换是这一领域研究方法上的重大突破。本文通过对小波变换理论的研究,分析了二维离散小波分解与重构的算法,提出了一种高频小波系数直接替换的算法,并利用MATLAB数学分析工具环境实现全色图像和多光谱图像的融合,实验结果表明这种方法能很好地增强图像的光谱分辨率,便于对图像进行分析和识别。     

Texture Analysis for Flaw Detection in Ultrasonic Images

In this paper, we present two approaches for flaw detection in TOFD (Time of Flight Diffraction) images based on texture features. Texture is one of the most important features used in recognizing patterns in an image. The paper describes texture features by two methods: Multiresolution analysis such as wavelet transforms and Gabor filters bank. The two-dimensional wavelet transform is used to decompose the input image into a multiresolution framework. The textural statistical parameters are used to allow the choice of the decomposition channel. The Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave. All Gabor filters can be generated from one mother wavelet by dilation and rotation. These filters represent an appropriate choice for tasks requiring simultaneous measurement in both space and frequency domains. The most relevant features are optimized by Principal Components Analysis (PCA) and used as input data on a Fuzzy C-Mean clustering classifier. We use two classes: ‘defects’ or ‘no defects’. The proposed approach is tested on the TOFD image achieved in an industrial field.     

Fuzzy clustering of Raman spectral imaging data with a wavelet-based noise-reduction approach

Raman spectral imaging has been widely used for extracting chemical information from biological specimens. One of the challenges is to cluster the chemical groups from the vast amount of hyperdimensional spectral imaging data so that functionally similar groups can be identified. In this paper, we present an approach that combines a differential wavelet-based data smoothing with a fuzzy clustering algorithm for the classification of Raman spectral images. The preprocessing of the spectral data is facilitated by decomposing them in the differential wavelet domain, where the discrimination of true spectral features and noise can be easily performed using a multi-scale pointwise product (MPP) criterion. This approach is applied to the classification of spectral data collected from adhesive/dentin interface specimens where the spectral data exhibit different signal-to-noise ratios. The proposed wavelet approach has been compared to several conventional noise-removal algorithms.     

基于谱直方图表示和支持向量机的纹理分类

在纹理分类中采用谱直方图表示(SHR),每个图像窗表示一个包含滤波后图像直方图的特征向量,而直方图是图像谱表示的连接桥梁.在滤波器选择算法之前,结合每个图像分块和滤波器的独立谱表示和直方图,可以获得更加低层的局部特征.最后,时所有独立滤波器采用滤波器选择算法来得到所需的少量滤波器.为了保证分类的可靠性,选择高斯径向基函数(RBF)进行谱直方图表示,采用支持向量机(SVMs)作为分类函数.对本文方法和其它两种方法:Gabor滤波和独立成分分析(ICA)进行了纹理分类和脸部识别的比较实验.实验结果表明,本文方法具有更高的分类准确性,也证明了SVMs优秀的泛化能力.