Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.     

Feature normalization via expectation maximization and unsupervised nonparametric classification for M-FISH chromosome images

Multicolor fluorescence in situ hybridization (M-FISH) techniques provide color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Chromosomes are stained combinatorially in M-FISH. By analyzing the intensity combinations of each pixel, all chromosome pixels in an image are classified. Often, the intensity distributions between different images are found to be considerably different and the difference becomes the source of misclassifications of the pixels. Improved pixel classification accuracy is the most important task to ensure the success of the M-FISH technique. In this paper, we introduce a new feature normalization method for M-FISH images that reduces the difference in the feature distributions among different images using the expectation maximization (EM) algorithm. We also introduce a new unsupervised, nonparametric classification method for M-FISH images. The performance of the classifier is as accurate as the maximum-likelihood classifier, whose accuracy also significantly improved after the EM normalization. We would expect that any classifier will likely produce an improved classification accuracy following the EM normalization. Since the developed classification method does not require training data, it is highly convenient when ground truth does not exist. A significant improvement was achieved on the pixel classification accuracy after the new feature normalization. Indeed, the overall pixel classification accuracy improved by 20% after EM normalization.     

Automatic segmentation of high-throughput RNAi fluorescent cellular images.

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems.     

基于面向对象的极化雷达影像分类

有效的PolSAR影像分类技术是PolSAR成功应用的基础,然而相比于比较成熟的PolSAR成像技术与系统设计,PolSAR影像分类技术的发展相对滞后,针对PolSAR影像面向对象分类研究中存在的问题,提出了一种新的结合多种目标极化分解、ReliefF-PSO_SVM和集成学习的PolSAR影像面向对象分类方法。该方法首先采用多种方法对PolSAR影像进行目标极化分解;然后将利用不同极化分解方法提取的极化参数组合成一幅多通道影像;接下来对多通道影像进行分割、特征提取;采用ReliefF-PSO_SVM算法进行特征选择,并保留适应度最高的N个特征子集进行分类,每一个特征子集对应一个分类结果;最后利用集成学习技术对各分类结果进行集成。以吉林省长春市部分区域为研究区,Radarsat2影像为数据源,将提出的方法应用于土地利用分类中,取得了较好的分类效果,总体精度和Kappa系数分别达到了85.06%和0.8006。此外,还构建了3种对比方法用于分类,对比结果进一步证明了所提方法在PolSAR影像分类中的优越性。     

Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data

It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected.      

基于改进人工蜂群正余弦优化的红外图像分割方法

针对红外图像背景复杂且分割难度较大等问题,提出了一种改进人工蜂群正余弦优化的红外图像阈值分割方法。首先是将二维Otsu函数作为蜂群算法的适应度函数;其次采用混沌对立的学习方法和差分进化的方法改进了初始化种群和蜜蜂搜索方程;然后利用改进的蜂群算法优化阈值,缩小阈值的搜索区域;最后利用正余弦法计算出全局最优解,该最优解即为分割的最佳阈值。实验结果表明:论文方法与Otsu法、k-means法、区域生长法以及分水岭法相比,图像目标区域分割的平均交并比为84.13,平均准确率为89.18,有效提高了红外图像的分割精度。     

Wavelet-based compression of M-FISH images

Multiplex fluorescence in situ hybridization (M-FISH) is a recently developed technology that enables multi-color chromosome karyotyping for molecular cytogenetic analysis. Each M-FISH image set consists of a number of aligned images of the same chromosome specimen captured at different optical wavelength. This paper presents embedded M-FISH image coding (EMIC), where the foreground objects/chromosomes and the background objects/images are coded separately. We first apply critically sampled integer wavelet transforms to both the foreground and the background. We then use object-based bit-plane coding to compress each object and generate separate embedded bitstreams that allow continuous lossy-to-lossless compression of the foreground and the background. For efficient arithmetic coding of bit planes, we propose a method of designing an optimal context model that specifically exploits the statistical characteristics of M-FISH images in the wavelet domain. Our experiments show that EMIC achieves nearly twice as much compression as Lempel-Ziv-Welch coding. EMIC also performs much better than JPEG-LS and JPEG-2000 for lossless coding. The lossy performance of EMIC is significantly better than that of coding each M-FISH image with JPEG-2000.     

基于区域MRF和贝叶斯置信传播的SAR图像分割

 本文通过定义新的势函数,将贝叶斯置信传播算法和区域MRF模型有效结合,提出了一种SAR图像分割算法.考虑到SAR图像丰富的纹理信息,该算法对分水岭分割后的过分割区域提取纹理特征,在得到的区域邻接图上构建MRF模型,并加入区域灰度均值和方差作为区域特征,利用FCM聚类的初分割结果定义区域的关联势函数,并将区域特征引入到置信传播算法中,定义了新的交互势函数.该算法充分利用了SAR图像空间的背景信息,所定义的新的交互势函数能在促进分割结果区域一致性的同时较好保护边缘.实验结果表明,相对于其他MRF模型分割算法,本文算法能取得更好的分割效果.     

A MRF model-based segmentation approach to classification formultispectral imagery

An unsupervised segmentation approach to classification of multispectral image is suggested here in Markov random field (MRF) frame work. This work generalizes the work of Sarkar et al. (2000) on gray value images for multispectral images and is extended for landuse classification. The essence of this approach is based on capturing intrinsic characters of tonal and textural regions of any multispectral image. The approach takes an initially oversegmented image and the original. multispectral image as the input and defines a MRF over region adjacency graph (RAG) of the initially segmented regions. Energy function minimization associated with the MRF is carried out by applying a multivariate statistical test. A cluster validation scheme is outlined after obtaining optimal segmentation. Quantitative evaluation of classification accuracy of test data for three illustrations are shown and compared with conventional maximum likelihood procedure. Comparison of the proposed methodology with a recent work of texture segmentation in the literature has also been provided. The findings of the proposed method are found to be encouraging     

Automatic detection of abnormalities in chest radiographs using local texture analysis

A fully automatic method is presented to detect abnormalities in frontal chest radiographs which are aggregated into an overall abnormality score. The method is aimed at finding abnormal signs of a diffuse textural nature, such as they are encountered in mass chest screening against tuberculosis (TB). The scheme starts with automatic segmentation of the lung fields, using active shape models. The segmentation is used to subdivide the lung fields into overlapping regions of various sizes. Texture features are extracted from each region, using the moments of responses to a multiscale filter bank. Additional "difference features" are obtained by subtracting feature vectors from corresponding regions in the left and right lung fields. A separate training set is constructed for each region. All regions are classified by voting among the k nearest neighbors, with leave-one-out. Next, the classification results of each region are combined, using a weighted multiplier in which regions with higher classification reliability weigh more heavily. This produces an abnormality score for each image. The method is evaluated on two databases. The first database was collected from a TB mass chest screening program, from which 147 images with textural abnormalities and 241 normal images were selected. Although this database contains many subtle abnormalities, the classification has a sensitivity of 0.86 at a specificity of 0.50 and an area under the receiver operating characteristic (ROC) curve of 0.820. The second database consist of 100 normal images and 100 abnormal images with interstitial disease. For this database, the results were a sensitivity of 0.97 at a specificity of 0.90 and an area under the ROC curve of 0.986.     

Image segmentation using a texture gradient based watershed transform

The segmentation of images into meaningful and homogenous regions is a key method for image analysis within applications such as content based retrieval. The watershed transform is a well established tool for the segmentation of images. However, watershed segmentation is often not effective for textured image regions that are perceptually homogeneous. In order to segment such regions properly, the concept of the "texture gradient" is introduced. Texture information and its gradient are extracted using a novel nondecimated form of a complex wavelet transform. A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. A marker driven watershed transform is then used to segment the identified regions properly. The combined algorithm produces effective texture and intensity based segmentation for application to content based image retrieval.     

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.     

基于空间极值和区域合并的图像分割

针对街景图像所包含的对象比较复杂的特点,该算法提出利用空间极值点作为分水岭分割算法的种子点,并采用区域邻接表来表示区域间复杂的邻区关系,实现了一种较为快速有效的、符合人眼感知特性的分割算法。首先,根据图像的空间极值点,对其进行分水岭分割;接着,根据分割之后的区域,进行简单合并;最后,根据视觉一致性、区域内和区域间像素间的差异程度,合并满足条件的区域,得到最终的分割图像。仿真结果表明,该算法的分割效果具有很好的鲁棒性、通用性及自适应性。     

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。     

Hybrid image segmentation using watersheds and fast region merging

A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds. An edge-preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude. This initial segmentation is the input to a computationally efficient hierarchical (bottom-up) region merging process that produces the final segmentation. The latter process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all RAG edges in a priority queue. We propose a significantly faster algorithm, which additionally maintains the so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, one-pixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with two-dimensional/three-dimensional (2-D/3-D) magnetic resonance images are presented.     

交叉熵约束的红外图像最小错误阈值分割

针对目标和背景具有相似统计分布的红外图像,经典阈值分割方法仅以某种形式的方差或熵作为准则,未考虑图像的实际特性,分割效果不甚理想。为此,提出了一种基于交叉熵约束的红外图像最小错误阈值分割新方法。首先,引入交叉熵来度量目标和背景统计分布的相似性,交叉熵越小表明分布越相似;然后在交叉熵小于一定值的条件下使分类错误达到最小。交叉熵的约束保证了分割过程适应红外图像实际特性,分类错误最小确保了分割效果的有效性。该方法原理清晰、参数设置简单,在一系列实际图像上的实验结果表明,与现有几种经典阈值分割方法相比,文中方法有效提高了目标和背景具有相似统计分布的红外图像的阈值分割准确率。     

采用奇异值分解的图像模糊区域检测分割新方法

针对局部模糊图像的模糊区域检测分割问题,提出了一种改进的基于奇异值分解和图像抠图的模糊区域自动检测分割算法。首先,采用分块的方法对局部模糊图像进行再次模糊,通过比较前后图像块的奇异值特征变化差异将其标识模糊块或清晰块以得到一个标识图。其次,根据标识图,结合图像抠图技术对图像的局部模糊区域进行自动提取。实验结果表明,该方法可以较为精确地检测并分割出局部模糊图像中的模糊区域。      

Unsupervised image classification, segmentation, and enhancementusing ICA mixture models

An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent, non-Gaussian densities. The algorithm estimates the data density in each class by using parametric nonlinear functions that fit to the non-Gaussian structure of the data. This improves classification accuracy compared with standard Gaussian mixture models. When applied to images, the algorithm can learn efficient codes (basis functions) for images that capture the statistically significant structure intrinsic in the images. We apply this technique to the problem of unsupervised classification, segmentation, and denoising of images. We demonstrate that this method was effective in classifying complex image textures such as natural scenes and text. It was also useful for denoising and filling in missing pixels in images with complex structures. The advantage of this model is that image codes can be learned with increasing numbers of classes thus providing greater flexibility in modeling structure and in finding more image features than in either Gaussian mixture models or standard independent component analysis (ICA) algorithms.     

基于医学图像分割的半自动ROI提取

针对当前ROI提取方法，提出了一种基于图像分割的半自动ROI提取算法，一方面保证提取出的ROI是有意义的对象，另一方面人为指定因素更小，对ROI提取的智能性有较大提高。在图像分割中提出一种基于改进的分水岭算法的医学图像分割方法，引入了浮点活动图像代替梯度图像进行分水岭变换，使分割结果边缘定位更准确，在分水岭变换之后，提出基于面积和对比度控制的合并小区域准则，可得到更好的分割效果。     

一种改进的Laplacian SVM的SAR图像分割算法

当有标识的样本数量有限时,Laplacian SVM算法需要加入尽量多的无标识样本,以提高分类精度.但同时当无标识样本数很大时,算法的时间和空间复杂度将难以接受.为了将Laplacian SVM应用于SAR图像分割这样的大规模分类问题中,提出了一种改进的Laplacian支持向量机算法(Improved Laplaci...