Design and Analysis of an Efficient Evolutionary Image Segmentation Algorithm

Evolutionary image segmentation algorithms have a number of advantages such as continuous contour, non-oversegmentation, and non-thresholds. However, most of the evolutionary image segmentation algorithms suffer from long computation time because the number of encoding parameters is large. In this paper, design and analysis of an efficient evolutionary image segmentation algorithm EISA are proposed. EISA uses a K-means algorithm to split an image into many homogeneous regions, and then uses an intelligent genetic algorithm IGA associated with an effective chromosome encoding method to merge the regions automatically such that the objective of the desired segmentation can be effectively achieved, where IGA is superior to conventional genetic algorithms in solving large parameter optimization problems. High performance of EISA is illustrated in terms of both the evaluation performance and computation time, compared with some current segmentation methods. It is empirically shown that EISA is robust and efficient using nature images with various characteristics.     

基于区域相似性与相异性的数字图像分割方法

针对数字图像数据量大、内容复杂、特征度量困难的特点,提出了一种综合区域相似性和相异性的基于图模型的分割方法.使用颜色方差作为距离度量,依靠区域邻接图和最近邻区域图来完成数字图像的快速区域合并分割.在合并过程中,通过合并区域的最小合并代价和最大合并代价变化,调整合并顺序,从策略上保证了分割区域的同质性和区域间的相异性.实验结果表明,该方法可以较好地解决图像的误分割现象.     

Sub-scene segmentation using constraints based on Gestalt principles

In this paper, an unsupervised sub-scene segmentation method is proposed. It emphasizes on generating more integrated and semantically consistent regions instead of homogeneous but detailed over-segmented regions usually produced by conventional segmentation methods. Several properties of sub-scenes are explored such as proximity grouping, area of influence, similarity and harmony based on psychological principles. These properties are formulated into constraints that are used directly in the proposed sub-scene segmentation. A self-determined approach is conducted to get the optimal segmentation result based on the characteristics of each image in an unsupervised manner. The proposed method is evaluated over three datasets. For quantitative evaluation, the performance of the proposed method is on par with state-of-the-art unsupervised segmentation methods; for qualitative evaluation, the proposed method handles various sub-scenes well, and produces neater results. The sub-scenes segmented by the proposed method are generally consistent with natural scene categories.     

Coupling orientation diffusion with coherence-enhancing diffusion: a fingerprint case

The enhancement of coherent flow-like structures is desired for many image processing tasks, such as segmentation and feature detection. This task can be accomplished in a natural way by adopting anisotropic diffusion filtering using a diffusion matrix adapted to the local structure. This method is referred to as coherence-enhancing diffusion (CED). The performance of CED can be analyzed by observing the evolution of the orientation field (OF) associated with an evolving diffusion matrix. It was revealed from a series of experiments that the final OF from a CED-enhanced image sometimes strays from its true underlying OF (marked by a human expert), degrading its performance. In this paper, a strategy is proposed which repeatedly cleans the OF associated with a diffusion matrix. Thus, for every iteration of CED, its OF is diffused separately until it converges and is then fed back to the CED process to move forward. This hypothesis is tested with the motive of getting an enhanced CED performance. The proposed scheme is validated using fingerprint data, and their numerical results are displayed.     

Color Texture Segmentation Based on the Modal Energy of Deformable Surfaces

This paper presents a new approach for the segmentation of color textured images, which is based on a novel energy function. The proposed energy function, which expresses the local smoothness of an image area, is derived by exploiting an intermediate step of modal analysis that is utilized in order to describe and analyze the deformations of a 3-D deformable surface model. The external forces that attract the 3-D deformable surface model combine the intensity of the image pixels with the spatial information of local image regions. The proposed image segmentation algorithm has two steps. First, a color quantization scheme, which is based on the node displacements of the deformable surface model, is utilized in order to decrease the number of colors in the image. Then, the proposed energy function is used as a criterion for a region growing algorithm. The final segmentation of the image is derived by a region merge approach. The proposed method was applied to the Berkeley segmentation database. The obtained results show good segmentation robustness, when compared to other state of the art image segmentation algorithms.     

A multiresolution image segmentation technique based on pyramidalsegmentation and fuzzy clustering

In this paper, an unsupervised image segmentation technique is presented, which combines pyramidal image segmentation with the fuzzy c-means clustering algorithm. Each layer of the pyramid is split into a number of regions by a root labeling technique, and then fuzzy c-means is used to merge the regions of the layer with the highest image resolution. A cluster validity functional is used to find the optimal number of objects automatically. Segmentation of a number of synthetic as well as clinical images is illustrated and two fully automatic segmentation approaches are evaluated, which determine the left ventricular volume (LV) in 140 cardiovascular magnetic resonance (MR) images. First fuzzy c-means is applied without pyramids. In the second approach the regions generated by pyramidal segmentation are merged by fuzzy c-means. The correlation coefficients of manually and automatically defined LV lumen of all 140 and 20 end-diastolic images were equal to 0.86 and 0.79, respectively, when images were segmented with fuzzy c-means alone. These coefficients increased to 0.90 and 0.93 when the pyramidal segmentation was combined with fuzzy c-means. This method can be applied to any dimensional representation and at any resolution level of an image series. The evaluation study shows good performance in detecting LV lumen in MR images.     

一种基于自适应标记与区域间近邻传播聚类的分水岭图像分割算法

分水岭算法是一种高效的图像分割算法,能够准确地对图像进行基于区域的分割,但是存在易过分割的问题.为此本文提出一种改进的分水岭算法：首先,对彩色图像进行频谱包络滤波并计算彩色梯度获得梯度图像,再采取一种自适应设定参数的H-minima技术,对梯度图像的极小值区域进行标记;然后,对已标记极小值区域的梯度图像进行分水岭分割;最后,计算分水岭分割所得各区域的颜色矩,作为该区域的颜色特征,并对这些区域进行近邻传播聚类获得分割结果.通过与近年来其它改进的分水岭算法和采用聚类的图像分割算法实验比较,本文所提算法能更加有效地抑制过分割,提高分割准确率,具有良好的自适应性和鲁棒性.     

Applications of Locally Orderless Images

In a recent work, J. J. Koenderink and A. J. Van Doorn considered a family of three intertwined scale-spaces coined the locally orderless image (LOI) (1999, J. Comput. Vision, 31 (2/3), 159–168). The LOI represents the image, observed at inner scale σ, as a local histogram with bin-width β, at each location, with a Gaussian-shape region of interest of extent α. LOIs form a natural and elegant extension of scale-space theory, show causal consistency, and enable the smooth transition between pixels, histograms, and isophotes. The aim of this work is to demonstrate the wide applicability and versatility of LOIs. We present applications for a range of image processing tasks, including new nonlinear diffusion schemes, adaptive histogram equalization and variations, several methods for noise and scratch removal, texture rendering, classification, and segmentation.     

基于图像分解的稀疏正则化多区域图像分割方法

针对图像具有不同特征的成分,提出一种基于图像分解的多区域图像分割模型和算法.首先将图像分解项引入到图像分割模型中,递减了纹理和噪声对分割的影响;其次使用稀疏正则化方法保持分割区域的边缘几何结构;最后基于增广Lagrange乘子法,给出一种由扩散流引导的小波迭代阈值图像分割算法.一系列实验结果表明,提出的方法抗干扰能力强,对噪声具有更好的鲁棒性.提出的方法不仅能够分割结构图像,并且能够分割较复杂的纹理图像.     

Unsupervised Texture Segmentation of Natural Scene Images Using Region-based Markov Random Field

In analyzing natural scene images, texture plays an important role because such images are full of various textures. Although texture is crucial information in analyzing natural scene images, the texture segmentation problem is still hard to solve since the texture often exhibit non-uniform statistical characteristics. Although there are several supervised approaches that partition an image according to pre-defined semantic categories, the ever-changing appearances in the natural images make such schemes intractable. To overcome this limitation, we propose a novel unsupervised texture segmentation method for natural images by using the Region-based Markov Random Field (RMRF) model which enforces the spatial coherence between neighbor regions. We introduce the concept of pivot regions which plays a decisive role to incorporate local data interaction. By forcing pivot regions to adhere to initial labels, we make the Markov Random Field evolve fast and precisely. The proposed algorithm based on the pivot regions and the MRF for encapsulating spatial dependencies between neighborhoods yields high performance for the unsupervised segmentation of natural scene images. Quantitative and qualitative evaluations prove that the proposed method achieves comparable results with other algorithms.     

基于高斯-马尔可夫随机场和神经网络的无监督纹理分割

提出一种基于高斯-马尔可夫随机场(GMRF)和神经网络的无监督纹理分割方法,方法分为两步:第一步先将图像分为不重叠的小块,在小块中计算GMRF参数,并将此参数和该块的均值、方差作为该块的特征向量,然后进行聚类,得到原图像的一个初始分割和图像中所包含的类别数;第二步构造一个决定性松弛的神经网络,将第一步得到的结果作为初始输入,经过神经网络计算,得到一个精确的分割结果.实验证明:该方法是一种有效的纹理分割方法.     

基于二阶梯度的红外小目标检测方法

为了提高复杂背景下红外图像中弱小目标的检测概率同时降低虚警率,本文提出一种基于二阶梯度的红外弱小目标检测算法。首先基于小面模型采用二维离散正交多项式对原始图像局部灰度分布进行拟合,然后设计二阶方向导数滤波器对图像进行滤波并分解为不同的方向通道,再根据极值定理对不同的方向通道求取极值图像,随后对极值图像进行形态学滤波以增强目标能量并进一步抑制背景杂波,之后对极值图像进行归一化处理并统计其直方图,利用直方图灰度分布选取合适的分割阈值,进行阈值分割后得到二值图像并最终确定目标位置。实验结果表明,该方法在信噪比极低的复杂背景下可准确地对弱小目标进行检测。     

基于非线性复扩散及全局和局部特性的医学图像角点检测

常规的角点检测方法通常只考虑曲率极大值的局部特性,且在单一尺度下进行,易受噪声的影响,造成角点的漏检。为了克服这些缺陷,本文采用将多尺度分析的非线性复扩散处理方法与边缘点曲率极值的局部和全局特性相结合进行角点检测的方法。首先对图像进行保护边缘的非线性复扩散,以获取不同尺度的图像信息;然后针对不同尺度下图像的实部和虚部,进行基于全局和局部特性的角点检测,除考虑角点曲率极值的局部特性外,还将其与邻近点曲率的关系作为全局特性加以比较,最终确定角点。实验结果表明,本文方法可以有效地去除噪声的干扰,提取的角点数目多,避免漏检,位置准确。     

融合层次聚类的高分辨率遥感影像超像素分割方法

为解决遥感影像分割尺度自动选取难的问题,提出了融合层次聚类的高分辨率遥感影像超像素分割方法。首先采用自适应形态重建的分水岭分割算法将影像分割成多个超像素;然后提取各超像素的灰度特征向量;最后利用层次聚类方法进行超像素合并,实现高分辨率遥感影像的精确分割。实验选用4组景遥感影像;采用定性和定量相结合的方法评价实验结果。实验结果表明,该方法有效提高了遥感影像分割精度,并取得了较好的分割视觉效果。     

Image segmentation via self-organising fusion

A method called self-organising fusion (SOF) for performing fast image segmentation is presented. The input image is divided into a set of small regions, each associated with a working feature. First, all regions are simultaneously updated and then a statistical process is applied to merge the qualified regions. The contours of objects are obtained by alternating the two processes of updating and merging until convergence. The concurrent updating creates a SOF behaviour that facilitates the identification of regions presumably comprising the same object. The method can save computation cost as both updating and merging are conducted in parallel fashion, and as parameter selection is done for local regions, it is able to deal with fairly complex images     

RAGS: region-aided geometric snake

An enhanced, region-aided, geometric active contour that is more tolerant toward weak edges and noise in images is introduced. The proposed method integrates gradient flow forces with region constraints, composed of image region vector flow forces obtained through the diffusion of the region segmentation map. We refer to this as the Region-aided Geometric Snake or RAGS. The diffused region forces can be generated from any reliable region segmentation technique, greylevel or color. This extra region force gives the snake a global complementary view of the boundary information within the image which, along with the local gradient flow, helps detect fuzzy boundaries and overcome noisy regions. The partial differential equation (PDE) resulting from this integration of image gradient flow and diffused region flow is implemented using a level set approach. We present various examples and also evaluate and compare the performance of RAGS on weak boundaries and noisy images.     

基于互补空间信息的多目标进化聚类图像分割

现有的多目标进化聚类算法应用于图像分割时,没有考虑图像的任何空间信息,使得该类算法在含噪图像上的分割性能不理想。该文鉴于图像的局部空间信息和非局部空间信息的互补性,试图将这两种空间信息同时引入到聚类有效性函数中,构造了融合互补空间信息的目标函数,进而提出了应用于图像分割的基于互补空间信息的多目标进化聚类算法。该算法采用染色体可变长编码策略在进化过程中自动确定图像分割数目,减少了人为干预。自然图像的分割实验表明,该算法不但能在含噪图像上取得较为满意的分割性能,而且适用于多种类型的含噪图像。     

基于逻辑校准的多分类残差网络的肺分割算法

针对图像噪声以及血管、支气管等因素引起的肺分割困难的问题,提出了一种基于逻辑校准的多分类残差网络分割算法.该算法将图像区域划分为肺、背景及边界三类,通过扩大不同类型间的差异来提升分割准确率.算法先将图像分割为固定尺寸区域,然后利用残差网络提取纹理特征进行分类训练与测试,实现粗分割.最后对边界区域阈值处理实现细分割.利用公开数据集对该算法进行了测试,实验结果表明,此分割算法在召回率、精确率以及交并比等方面均优于当下前沿的分割网络之一的U-Net,分别达到99.79％,98.13％和97.83％,可为后续的肺部疾病临床诊断提供参考依据.     

基于红外CCD的掺钆液体闪烁体液位测量方法

图像分割在医学超声图像的定量、定性分析中均扮演着十分重要的作用, 并直接影响到后续的分析、处理工作。针对医学超声图像对比度低和噪声强的特点, 提出了一种将超像素和模糊聚类技术相结合的图像分割方法。该方法利用简单线性迭代聚类算法产生多个超像素子区域, 通过比较各个子区域间特征向量的相似性, 利用模糊C均值(FCM)聚类技术对这些过分割区域进行合并, 实现超声图像目标区域的有效分割。和传统的基于单像素的FCM聚类算法相比, 该方法具有较强的鲁棒性, 有效提高了目标区域的分割精度和分割效率, 取得了较好的分割效果。