基于数学形态学的SAR图像分割方法

图像分割是遥感图像处理中很重要的一步。因SAP图像通常带有较强的嗓声,用传统的边缘检测方法效果不理想。作者利用数学形态学开闭运算和混合滤波,可据目标的形状选用算法中的探针,取得了较好的滤波去噪和目标分割的效果。     

Fuzzy mathematical morphology

A new morphology is proposed which uses fuzzy structuring elements and is internal on fuzzy sets. It is fully compatible with conventional morphology which uses binary structuring elements, either on binary or on grey-tone sets. The properties of the two basic operations, fuzzy dilation and fuzzy erosion, are presented. An example showing the interest of fuzzy morphology to manipulate the uncertainty linked to spatial information is presented in multisource medical image data fusion.     

DBSCAN算法与数学形态学在岩石薄片图像分割中的应用

在岩石薄片图像处理中,针对岩屑矿物个数未知、成像多为聚集的点状的特点,提出了一种基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法与数学形态学的矿物分割方法。首先,标记出每个颗粒目标,求得其中心坐标;其次,利用DBSCAN算法对岩屑颗粒目标中心进行聚类,将不同区域的岩屑目标分离出来;最后,利用数学形态学方法对聚类结果做膨胀、填孔、腐蚀等处理,得到颗粒的边界。实验分析表明:该方法聚类效果良好,参数容易控制并有一定的抗噪性能,对岩屑颗粒目标的提取有较好的效果。     

Fuzzy c-means clustering with weighted image patch for image segmentation

Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide-spread prevalence, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, we propose the weighted image patch-based FCM (WIPFCM) algorithm for image segmentation. In this algorithm, we use image patches to replace pixels in the fuzzy clustering, and construct a weighting scheme to able the pixels in each image patch to have anisotropic weights. Thus, the proposed algorithm incorporates local spatial information embedded in the image into the segmentation process, and hence improve its robustness to noise. We compared the novel algorithm to several state-of-the-art segmentation approaches in synthetic images and clinical brain MR studies. Our results show that the proposed WIPFCM algorithm can effectively overcome the impact of noise and substantially improve the accuracy of image segmentations.     

Fuzzy modified cuckoo search for biomedical image segmentation

Knowledge and Information Systems - In this article, a new method is proposed for biomedical image segmentation. The proposed method for biomedical image segmentation will be known as fuzzy...     

Fuzzy Markov random fields versus chains for multispectral image segmentation

This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (mode of posterior marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data     

Fuzzy spectral clustering with robust spatial information for image segmentation

In recent years, spectral clustering has become one of the most popular clustering algorithms in areas of pattern analysis and recognition. This algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data, and is simple to implement. However, when the image is corrupted by noise, spectral clustering cannot obtain satisfying segmentation performance. In order to overcome the noise sensitivity of the standard spectral clustering algorithm, a novel fuzzy spectral clustering algorithm with robust spatial information for image segmentation (FSC_RS) is proposed in this paper. Firstly, a non-local-weighted sum image of the original image is generated by utilizing the pixels with a similar configuration of each pixel. Then a robust gray-based fuzzy similarity measure is defined by using the fuzzy membership values among gray values in the new generated image. Thus, the similarity matrix obtained by this measure is only dependent on the number of the gray-levels and can be easily stored. Finally, the spectral graph partitioning method can be applied to this similarity matrix to group the gray values of the new generated image and then the corresponding pixels in the image are reclassified to obtain the final segmentation result. Some segmentation experiments on synthetic and real images show that the proposed method outperforms traditional spectral clustering methods and spatial fuzzy clustering in efficiency and robustness.     

Multiscale joint segmentation and registration of image morphology

Multimodal image registration significantly benefits from previous denoising and structure segmentation and vice versa. In particular combined information of different image modalities makes segmentation significantly more robust. Indeed, fundamental tasks in image processing are highly interdependent. A variational approach is presented, which combines the detection of corresponding edges, an edge preserving denoising and the morphological registration via a non-rigid deformation for a pair of images with structural correspondence. The morphology of an image function is split into a singular part consisting of the edge set and a regular part represented by the field of normals on the ensemble of level sets. A Mumford-Shah type free discontinuity problem is applied to treat the singular morphology and the matching of corresponding edges under the deformation. The matching of the regular morphology is quantified by a second contribution which compares deformed normals and normals at deformed positions. Finally, a nonlinear elastic energy controls the deformation itself and ensures smoothness and injectivity. A multi scale approach that is based on a phase field approximation leads to an effective and efficient algorithm. Numerical experiments underline the robustness of the presented approach and show applications on medical images.     

Robust iris segmentation on uncalibrated noisy images using mathematical morphology

This paper proposes a new approach for fast iris segmentation that relies on the closed nested structures of iris anatomy (the sclera is brighter than the iris, and the iris is brighter than the pupil) and on its polar symmetry. The described method applies mathematical morphology for polar/radial-invariant image filtering and for circular segmentation using shortest paths from generalized grey-level distances. The proposed algorithm obtained good results on the NICE-I contest and showed a very robust behavior, especially when dealing with half-closed eyes, different skin colours/illumination or subjects wearing glasses.     

Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology

Document layout analysis or page segmentation is the task of decomposing document images into many different regions such as texts, images, separators, and tables. It is still a challenging problem due to the variety of document layouts. In this paper, we propose a novel hybrid method, which includes three main stages to deal with this problem. In the first stage, the text and non-text elements are classified by using minimum homogeneity algorithm. This method is the combination of connected component analysis and multilevel homogeneity structure. Then, in the second stage, a new homogeneity structure is combined with an adaptive mathematical morphology in the text document to get a set of text regions. Besides, on the non-text document, further classification of non-text elements is applied to get separator regions, table regions, image regions, etc. The final stage, in refinement region and noise detection process, all regions both in the text document and non-text document are refined to eliminate noises and get the geometric layout of each region. The proposed method has been tested with the dataset of ICDAR2009 page segmentation competition and many other databases with different languages. The results of these tests showed that our proposed method achieves a higher accuracy compared to other methods. This proves the effectiveness and superiority of our method.     

基于数学形态学的文档图像倾斜校正算法

随着信息采集技术的不断发展,文档图像在信息的数字化管理中越来越重要.对文档图像的倾斜校正进行了研究,给出了基于数学形态学和Hough变换相结合的算法,进行文档图像的倾斜校正,同时将算法应用于印刷体和手写体的文档图像.实验表明该算法可以有效应用于两种文档图像的倾斜校正.     

Teeth segmentation in digitized dental X-ray films using mathematical morphology

Automating the process of postmortem identification of individuals using dental records is receiving increased attention. Teeth segmentation from dental radiographic films is an essential step for achieving highly automated postmortem identification. In this paper, we offer a mathematical morphology approach to the problem of teeth segmentation. We also propose a grayscale contrast stretching transformation to improve the performance of teeth segmentation. We compare and contrast our approach with other approaches proposed in the literature based on a theoretical and empirical basis. The results show that in addition to its capability of handling bitewing and periapical dental radiographic views, our approach exhibits the lowest failure rate among all approaches studied.     

Historical overview of image analysis and mathematical morphology

The paper consists in a survey of image analysis over the last fifty years, seen from the point of view of Mathematica Morphology.     

A programmable systolic device for image processing based on mathematical morphology

Systolic array architectures are favourable for special purpose systems as they are simple and offer a high degree of concurrency. A programmable systolic device is designed to cater for all tasks of image processing based on mathematical morphology. The design consists of a systolic memory matrix accessible via a rotation operation by a linear systolic array of simple processing elements. The instruction set consists of 1-bit assignmments, logical and and or and shift operations on the memory. Thus extremely short clock cycles and a high degree of parallelism can be achieved.     

Novel initialization scheme for Fuzzy C-Means algorithm on color image segmentation

This paper presents a novel initialization scheme to determine the cluster number and obtain the initial cluster centers for Fuzzy C-Means (FCM) algorithm to segment any kind of color images, captured using different consumer electronic products or machine vision systems. The proposed initialization scheme, called Hierarchical Approach (HA), integrates the splitting and merging techniques to obtain the initialization condition for FCM algorithm. Initially, the splitting technique is applied to split the color image into multiple homogeneous regions. Then, the merging technique is employed to obtain the reasonable cluster number for any kind of input images. In addition, the initial cluster centers for FCM algorithm are also obtained. Experimental results demonstrate the proposed HA initialization scheme substantially outperforms other state-of-the-art initialization schemes by obtaining better initialization condition for FCM algorithm.     

基于HSI空间模糊聚类的彩色图像分割新方法

模糊C均值(FCM)被广泛应用于彩色图像分割中,但传统的模糊C均值由于没有考虑空间信息,因此对噪声特别敏感。针对此问题,提出了一种在HIS颜色空间结合像素邻域空间信息的模糊聚类新方法。实验结果表明,此方法对高噪声图像有较好的处理结果。     

Enhancing effective depth-of-field by image fusion using mathematical morphology

Reduced depth-of-field (DOF) poses a problem in the light optical imaging system, since the objects present outside this zone appear blurry in the recorded image. The effective DOF of the sensor may be enhanced considerably without compromising the quality of the image by fusing images captured with different focused regions. This paper presents an image fusion technique suitable for combining multifocus images of a scene. The method employs morphological filters to select sharply focused regions from various images and then combines them together to reconstruct the image in which all the regions are properly focused. A performance measure based on image gradients is used to compare the results obtained by the proposed method with those obtained by other image fusion techniques.     

基于灰度形态学的红细胞图像边缘检测

边缘包含了图像很多重要的信息,边缘检测的好坏也直接决定了后续处理的效果。在利用数学形态学进行边缘检测后,会出现不需要的边缘,形成噪声。针对这种情况,提出一种计算中心像素与邻域像素均方差的方法对形态学边缘检测算子进行改进,该方法可以有效减少噪声,为以后对红细胞图像进行特征提取和分类打下良好的基础。