显微细胞图像的自动分割

由于细胞组织本身的复杂特性以及显微镜的影响,细胞图像的分割成为图像分割中的一大难题,考虑到边缘检测可以准确的为分割提供边缘点,提出了基于区域一致性测度的边缘检测算法,针对细胞图像的特点,实现其初始轮廓的自动获取,运用活动轮廓模型对初始轮廓进行优化,从而达到在大样本条件下直接对彩色细胞图像自动分割的目的。     

A method of boundary determination in digital images of urothelial cells

Digital images acquired by high resolution scanning of urothelial cells are segmented into nuclear and cytoplasmic regions. A method which is a hybrid of a gradient tracking method and a thresholding method is used, and criteria are presented to automatically detect failures of the algorithm. Tests on a sample of benign and malignant urothelial cells show a significant improvement in both accuracy and reliability as compared with a single segmentation method.     

Using active learning to annotate microscope images of parasite eggs

Microscopic analysis forms an integral part of many scientific studies. It is a task which requires great expertise and care. However, it can often be an extremely repetitive and labourious task. In some cases many hundreds of slides may need to be analysed, a process that will require each slide to be meticulously examined. Machine vision tools could be used to help assist in just such repetitive and tedious tasks. However, many machine vision solutions involve a lengthy data acquisition phase and in many cases result in systems that are highly specialised and not easily adaptable. In this paper, we describe a framework that applies flexible machine vision techniques to microscope analysis and utilises active learning to help overcome the data acquisition and adaptability problems. In particular we investigate the potential of various aspects of our proposed framework on a particular real world microscopic task, the recognition of parasite eggs.     

Segmentation based compression for graylevel images

This paper presents a segmentation based lossy image compression (SLIC) algorithm. The segmentation scheme (Biswas and Pal, Pattern Recog. Lett. 21 (2000)), entropy based and hierarchical in nature, provides sub-images of homogeneous regions. The compression algorithm encodes a graylevel image through global approximations of sub-images by 2-d Bezier-Bernstein polynomial along with corrections, if needed, over regions in sub-images by local approximation; contours by 1-d Bezier-Bernstein polynomial and texture, if present, by Huffman coding scheme using Hilbert scan on texture blocks. Order of the 2-d polynomials has been computed with the help of an image quality index (IQI). The proposed compression algorithm also examines the compression result by encoding contours through their approximation based on stretching of discrete circular arcs. Stretching is done by affine transformation. Compression results in both the cases have been compared with JPEG results. Attempts have been made to evaluate the quality of reconstructed images through a fidelity vector whose components are different objective measures.     

Segmentation and classification of hyperspectral images using watershed transformation

Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invaluable source of information regarding the physical nature of the different materials, leading to the potential of a more accurate classification. However, high dimensionality of hyperspectral data, usually coupled with limited reference data available, limits the performances of supervised classification techniques. The commonly used pixel-wise classification lacks information about spatial structures of the image. In order to increase classification performances, integration of spatial information into the classification process is needed. In this paper, we propose to extend the watershed segmentation algorithm for hyperspectral images, in order to define information about spatial structures. In particular, several approaches to compute a one-band gradient function from hyperspectral images are proposed and investigated. The accuracy of the watershed algorithms is demonstrated by the further incorporation of the segmentation maps into a classifier. A new spectral-spatial classification scheme for hyperspectral images is proposed, based on the pixel-wise Support Vector Machines classification, followed by majority voting within the watershed regions. Experimental segmentation and classification results are presented on two hyperspectral images. It is shown in experiments that when the number of spectral bands increases, the feature extraction and the use of multidimensional gradients appear to be preferable to the use of vectorial gradients. The integration of the spatial information from the watershed segmentation in the hyperspectral image classifier improves the classification accuracies and provides classification maps with more homogeneous regions, compared to pixel-wise classification and previously proposed spectral-spatial classification techniques. The developed method is especially suitable for classifying images with large spatial structures.     

一种基于边缘梯度的图像分割方法

利用Krisch边缘方向算子，实现了对彩色和服图片中的和服边缘检测，同时根据真实边界点处的梯度强度值大于其左右邻域的梯度强度值的特征，获得了边缘细化。在结合边界跟踪技术的基础上，有效地去除了和服中大量的与和服分割无关的花瓣图案，得到了和服的有效边缘。实用的边缘连接技术则成功地实现了和服图片中和服各部分的分割。实验结果表明该方法在和服分割上是一种实用的和成功的方法。     

支票大写金额图像分割策略

字符图像的分割是票据自动处理系统中一个预处理过程,提取字符的质量对识别系统有很大的影响。我国支票大写金额图像背景中存在定位线和背景纹理,还有各式各样的印章对笔画信息的干扰。该文解决了复杂背景下二值化问题,采用形态学方法去除定位线,根据目标与背景的灰度和双边缘特征分析进行二值化处理,利用用户填写字符串时的书写习惯去除孤立印章碎块,基于区域增长的后处理过程来修补前期处理过程中断裂的笔画。该方法在真实支票图像的获取上,取得了较为满意的结果。     

基于边缘分割的多光谱图像融合方法

对于高分辨率的全色图像与低分辨率的多光谱图像,通常采用传统的IHS方法进行融合。这种融合方法能很好地提高低分辨率图像的清晰度,但却容易扭曲原始的光谱特性,产生光谱退化现象。针对这一问题,本文采用高分辨率与多光谱图像的边缘分割信息对I分量进行调制,使新生成的I分量保持原始的光谱特性。通过融合实验结果表明,这种方法比传统的IHS方法有效地改善了融合图像光谱失真的现象。     

Segmentation using Appearance of Mesostructure Roughness

This paper introduces mesostructure roughness as an effective cue in image segmentation. Mesostructure roughness corresponds to small-scale bumps on the macrostructure (i.e., geometry) of objects. Specifically, the focus is on the texture that is created by the projection of the mesostructure roughness on the camera plane. Three intrinsic images are derived: reflectance, smooth-surface shading and mesostructure roughness shading (meta-texture images). A constructive approach is proposed for computing a meta-texture image by preserving, equalizing and enhancing the underlying surface-roughness across color, brightness and illumination variations. We evaluate the performance on sample images and illustrate quantitatively that different patches of the same material, in an image, are normalized in their statistics despite variations in color, brightness and illumination. We develop an algorithm for segmentation of an image into patches that share salient mesostructure roughness. Finally, segmentation by line-based boundary-detection is proposed and results are provided and compared to known algorithms.     

图像阈值分割方法的比较与分析

本文在介绍了三种具有代表性的图像阈值分割法的基础上,通过对它们的性能进行分析比较,给出了 这三种阈值分割法的适用范围。最后,针对三种分割法的不足,提出了一种基于边缘检测与局部直方 图的图像分割算法。     

Segmentation of chromatic images

When analyzing color pictures one often requires that the image be segmented into meaningful regions based upon the color characteristics of the scene. Such a problem can assume two different forms. The first variation arises when particular color space characteristics are known and the goal is to detect and extract image regions which possess the given color characteristics. The second case arises when there is no a priori knowledge about the color space characteristics of the scene and the goal is to segment the scene into meaningful regions which possess uniform color space characteristics. This paper describes an interactive system which uses a decision surface modeling approach to solve the first case and uses clustering techniques in the three-dimensional color space to solve the second case. A set of examples is presented and the performance of the system is evaluated.     

无监督纹理遥感图像分形分割仿真

针对无监督纹理遥感图像分形分割时,仅利用光谱信息,没有充分利用遥感图像中的纹理信息造成过分割或是欠分割的问题,提出结合光谱信息和纹理信息的无监督遥感图像分形分割方法。方法首先借助于双边滤波算法建立双边滤波模型,对无监督遥感图像进行平滑处理,利用边缘检测算子获取无监督遥感图像的纹理梯度和光谱梯度,保留遥感图像的纹理信息。将图像纹理梯度和光谱梯度的内积范数作为K-means算法的距离测度,针对K-means算法易陷入局部寻优问题,将遗传算法与K-means算法相结合实现全局寻优,完成无监督纹理遥感图像分形分割。通过在高分辨率遥感图像上的分割对比实验,证明上述方法能充分利用遥感图像的光谱和纹理信息,分割效果良好。     

Segmentation and tracking of piglets in images

An algorithm was developed for the segmentation and tracking of piglets and tested on a 200-image sequence of 10 piglets moving on a straw background. The image-capture rate was 1 image/140 ms. The segmentation method was a combination of image differencing with respect to a median background and a Laplacian operator. The features tracked were blob edges in the segmented image. During tracking, the piglets were modelled as ellipses initialised on the blobs. Each piglet was tracked by searching for blob edges in an elliptical window about the piglet's position, which was predicted from its previous two positions.     

基于Gabor小波变换的无监督纹理图像分割

本文提出了一种基于Ｇａｂｏｒ小波变换的无监督纹理分割方法,该方法首先对纹理图像进行Ｇａｂｏｒ小波变换,再经过一组Ｇａｕｓｓ波波器滤波,然后将得到的纹理特征送入由Ｈｏｐｆｉｅｌｄ网络构成的无监督分类器,最后得到纹理分割结果。实验和分析表明,该方法对于不同的纹理图像都具有良好的分割效果。     

Automatic joint classification and segmentation of whole cell 3D images

We present a machine learning tool for automatic texton-based joint classification and segmentation of mitochondria in MNT-1 cells imaged using ion-abrasion scanning electron microscopy (IA-SEM). For diagnosing signatures that may be unique to cellular states such as cancer, automatic tools with minimal user intervention need to be developed for analysis and mining of high-throughput data from these large volume data sets (typically ). Challenges for such a tool in 3D electron microscopy arise due to low contrast and signal-to-noise ratios (SNR) inherent to biological imaging. Our approach is based on block-wise classification of images into a trained list of regions. Given manually labeled images, our goal is to learn models that can localize novel instances of the regions in test datasets. Since datasets obtained using electron microscopes are intrinsically noisy, we improve the SNR of the data for automatic segmentation by implementing a 2D texture-preserving filter on each slice of the 3D dataset. We investigate texton-based region features in this work. Classification is performed by k-nearest neighbor (k-NN) classifier, support vector machines (SVMs), adaptive boosting (AdaBoost) and histogram matching using a NN classifier. In addition, we study the computational complexity vs. segmentation accuracy tradeoff of these classifiers. Segmentation results demonstrate that our approach using minimal training data performs close to semi-automatic methods using the variational level-set method and manual segmentation carried out by an experienced user. Using our method, which we show to have minimal user intervention and high classification accuracy, we investigate quantitative parameters such as volume of the cytoplasm occupied by mitochondria, differences between the surface area of inner and outer membranes and mean mitochondrial width which are quantities potentially relevant to distinguishing cancer cells from normal cells. To test the accuracy of our approach, these quantities are compared against manually computed counterparts. We also demonstrate extension of these methods to segment 3D images obtained using electron tomography.     

基于Gabor多通道滤波和Hopfield神经网络的纹理图象分割

文章针对纹理图象的特点,提出了一种基于Gabor多通道滤波和Hopfield神经网络的纹理图象的分割算法。首先构造一组Gabor滤波器（2－D）提取纹理图象多分辨率和多方向性的空域和频域特征。为了使纹理特征更加明显,在此基础上对滤波图象进行非线性变换,最后利用Hopfield神经网络通过松弛迭代算法实现纹理图象的快速分割,取得了良好的分割效果。     

基于离散平稳小波变换和FCM的纹理图像分割

采用离散平稳小波变换对纹理图像进行分解,以各层小波系数中能量为特征相向量,采用模糊c-均值聚类（FCM）对图像分割,并对分割方法进行了改进,提出采用网格法,将图像分解成若干子图像,对图像进行粗分割,再对边缘部分的网格进行细分的两步分割法。试验结果表明该方法显著提高了分割速度和精度。     

LIDAR点云中高精度建筑物分割算法研究

本文在深入研究已有基于Lidar点云的建筑物分割算法基础上,提出了一种结合高程纹理、边缘追踪和规则几何元素检测的建筑物目标分割算法,讨论了该算法在大数量点云处理过程中实现的可行性,总结了算法的实现步骤。通过实际数据的实验,验证了算法的可实现性,并对提取的建筑物目标给出了精度评价。     

一种基于分水线和先验知识的集成电路图像分割方法

在Watershed的分割图像基础上,使用贝叶斯理论的图像分割方法。首先对原始图像进行Watershed变换,然后在变换后的标注图像上进行能量的计算,通过选择最小能量的目标依次找出最理想的目标区域。设计一个先验密度来惩罚图像当中Watershed变换后相似的区域,图像分割进而变成对目标子集的最大后验估计。这样就可以逐步找出最理想目标区域和背景区域。实验结果证明,该方法有较好的分割结果。