Joint segmentation and pairing of multispectral chromosome images

Automated segmentation of touching or overlapping chromosomes in a metaphase image is a critical step for computer-aided chromosomes analysis. Conventional chromosome imaging methods acquire single-band grayscale images, and such a limitation makes the separation of touching or overlapping chromosomes challenging. In the multiplex fluorescence in situ hybridization (M-FISH) technique, each class of chromosomes can bind with a different combination of fluorophores. The M-FISH technique results in multispectral chromosome images, which has distinct spectral signatures. This paper presents a novel automated chromosome analysis method to combine the pixel-level geometric and multispectral information with decision-level pairing information. Our chromosome segmentation method uses the geometric and spectral information to partition the chromosome cluster into three regions. There will be ambiguity when combining these regions into separated chromosomes by using only spectral and geometric information. Then a graph–theoretical pairing method is introduced to resolve any remaining ambiguity of the aforementioned segmentation process. Experimental results demonstrate that the proposed joint segmentation and pairing method outperforms conventional grayscale and multispectral segmentation methods in separating touching and overlapping chromosomes.     

A new segmentation system for brain MR images based on fuzzy techniques

This work concerns a new method called fuzzy membership C-means (FMCMs) for segmentation of magnetic resonance images (MRI), and an efficient program implementation of it to the segmentation of MRI. Classical unsupervised clustering methods including the FCM by Bezdek, suffer many problems that can be partially treated with a proper rule to construct the initial membership matrix to clusters. This work develops a specific method to construct the initial membership matrix to clusters in order to improve the strength of the clusters. The new FMCM is tested on a set of benchmarks and then the application to the segmentation of MR images is presented and compared with the results obtained using FCM.     

改进的模糊C-均值算法在医学图像分割中的应用

针对随机选取聚类中心易使得迭代过程陷入局部最优解的缺点,提出了一种混合优化蚁群和动态模糊C-均值的图像分割方法,该方法利用蚁群算法较强处理局部极值的能力,并能动态确定聚类中心和数目.针对传统的分阶段结合遗传算法和蚁群算法的策略存在收敛速度慢,聚类精度差的问题,提出在整个优化过程综合遗传算法和蚁群算法,并在蚁群算法中引入拥挤度函数,利用遗传算法的快速性、全局收敛性提高了蚁群算法的收敛速度,同时利用蚁群算法的并行性和正反馈性提高了聚类的精确度.最后将该算法应用到医学图像分割,对比实验表明,混合算法具有很强的模糊边缘和微细边缘分割能力.     

改进的遗传模糊聚类算法对医学图像的分割

利用遗传算法全局随机搜索的特点,可以解决模糊C均值聚类（FCM）算法在医学图像分割中容易陷入局部最优解的问题,但确定遗传算法的初始搜索范围时,需要借助于人的经验。为此,用收敛速度快的硬聚类算法得到的聚类中心作为参考,上下浮动划出一个较小的数据范围,作为遗传算法的初始搜索空间。该方法在避免FCM算法陷入局部最优化的同时,也加速了遗传算法的收敛过程。实验表明,该方法相对于标准的遗传模糊算法,效果要好得多。     

Interactive chromaticity mapping for multispectral images

Multispectral images record detailed color spectra at each image pixel. To display a multispectral image on conventional output devices, a chromaticity mapping function is needed to map the spectral vector of each pixel to the displayable three dimensional color space. In this paper, we present an interactive method for locally adjusting the chromaticity mapping of a multispectral image. The user specifies edits to the chromaticity mapping via a sparse set of strokes at selected image locations and wavelengths, then our method automatically propagates the edits to the rest of the multispectral image. The key idea of our approach is to factorize the multispectral image into a component that indicates spatial coherence between different pixels, and one that describes spectral coherence between different wavelengths. Based on this factorized representation, a two-step algorithm is developed to efficiently propagate the edits in the spatial and spectral domains separately. The method presented provides photographers with efficient control over color appearance and scene details in a manner not possible with conventional color image editing. We demonstrate the use of interactive chromaticity mapping in the applications of color stylization to emulate the appearance of photographic films, enhancement of image details, and manipulation of different light transport effects.     

Feature based fuzzy inference system for segmentation of low-contrast infrared ship images

Segmentation of infrared ship target is important for sea surveillance system. However, as a result of the deficiencies of infrared images, the segmentation of infrared ship image becomes a challenge. For the purpose of addressing this problem, a feature based infrared ship image segmentation method utilizing the fuzzy inference system is proposed. Firstly, the intensity feature is extracted by applying unimodal threshold, which could preserve the low-contrast pixels in the infrared images. Secondly, the local spatial feature is extracted by employing saliency detection, region growing and morphology processing, which could express the shape of the target. Thirdly, the global spatial feature is extracted by utilizing partial region growing and weighted distance transformation, which could suppress the background. Then these features are fuzzified using accommodative ways and prior knowledge. And in light of the fuzzy rules based upon expert knowledge, these fuzzified features are integrated in fuzzy inference system. Finally, the complete target could be directly segmented from the output of the fuzzy inference system. Experimental results illustrate that the proposed method could effectively extract more intact targets from the low-contrast infrared ship images. Additionally, the proposed method outperforms some existed segmentation methods.     

基于MRF的模糊聚类的二维直方图医学图像分割

针对医学图像分割,由于核磁共振成像的热/电噪声影响,采用马尔可夫随机场(MRF)作为先验模型提取出医学图像的后验能量场,然后采用模糊C-均值聚类法找到医学图像的不同组织和背景的聚类中心,再利用二维直方图的方法,找到多阈值分割的各个阈值点进行分割.实验结果表明,此算法能够充分考虑图像中像素的空间相关性,并且可以很好地抑制噪声,针对医学图像分割具有很好的稳健性.     

基于模糊马尔可夫场的脑部MR图像分割算法

在传统马尔可夫场模型的基础上,建立了模糊马尔可夫场模型。通过对模型的分析得出图像像素对不同类的隶属度计算公式,提出了一种高效、无监督的图像分割算法,从而实现了对脑部MR图像的精确分割。通过对模拟脑部MR图像和临床脑部MR图像分割实验,表明新算法比传统的基于马尔可夫场的图像分割算法和模糊C-均值等图像分割算法有更精确的图像分割能力。     

A snake for model-based segmentation of biomedical images

In this work we present a snake based approach for the segmentation of images of computerized tomography (CT) scans. We introduce a new term for the internal energy and another one for external energy which solve common problems associated with classical snakes in this type of images. A simplified minimizing method is also presented.     

A non-parametric segmentation methodology for oral videocapillaroscopic images

We aim to describe a new non-parametric methodology to support the clinician during the diagnostic process of oral videocapillaroscopy to evaluate peripheral microcirculation. Our methodology, mainly based on wavelet analysis and mathematical morphology to preprocess the images, segments them by minimizing the within-class luminosity variance of both capillaries and background. Experiments were carried out on a set of real microphotographs to validate this approach versus handmade segmentations provided by physicians. By using a leave-one-patient-out approach, we pointed out that our methodology is robust, according to precision–recall criteria (average precision and recall are equal to 0.924 and 0.923, respectively) and it acts as a physician in terms of the Jaccard index (mean and standard deviation equal to 0.858 and 0.064, respectively).     

一种新的粘连字符图像分割方法

针对监控画面采样图像中数字的自动识别问题,提出一种新的粘连字符图像分割方法。该方法以预处理后二值图像的连通状况来判定字符粘连的存在,并对粘连字符图像采用上下轮廓极值法确定候选粘连分割点,以双向最短路径确定合适的图像分割线路。仿真实验表明,该方法能有效解决粘连字符图像的分割问题。     

Edge detection in multispectral images

This paper proposes an extension of edge detectors based on second-order differential operators to the case of multiple band (color) images. To this end, we define a local directional measure of multispectral contrast. A definition of edge point is then given, as the location of a directional maximum of the contrast function. Extremal contrast edges are defined, as loci of zero crossing points of the first directional derivative of the contrast function. The qualitative properties of the so defined edges are briefly discussed. An algorithm for edge location with subpixel resolution is proposed, with an example of application to actual RGB imagery.     

多层嵌入的多光谱图像可逆信息隐藏算法

针对现有可逆信息隐藏算法嵌入容量偏低、不适合多光谱图像等问题,提出一种基于预测误差直方图移位和波段顺序重排的多层嵌入的多光谱图像可逆信息隐藏算法。对于每层嵌入,首先用提出的排序算法对波段进行重新排序,然后利用多光谱图像的空间相关性和谱间相关性对图像进行自适应联合预测,最后通过预测误差直方图移位技术嵌入秘密信息。Landsat卫星多光谱图像仿真结果表明,提出的算法和典型可逆信息隐藏算法相比具有更好的视觉质量和更大的隐藏容量。     

A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data

The objective function of the original (fuzzy) c-mean method is modified by a regularizing functional in the form of total variation (TV) with regard to gradient sparsity, and a regularization parameter is used to balance clustering and smoothing. An alternating direction method of multipliers in conjunction with the fast discrete cosine transform is used to solve the TV-regularized optimization problem. The new algorithm is tested on both synthetic and real data, and is demonstrated to be effective and robust in treating images with noise and missing data (incomplete data).     

基于空间约束的模糊C均值聚类肝脏CT图像分割

传统模糊C均值（FCM）聚类算法应用于肝脏CT图像分割时仅考虑像素本身特征,无法解决灰度不均匀造成的影响以及肝脏边界模糊造成的边界泄露的问题。为解决上述问题,提出一种结合空间约束的模糊C均值（SFCM）聚类分割算法。首先,使用二维高斯分布函数构建卷积核,利用该卷积核对源图像进行空间信息提取得到特征矩阵;然后,引入空间约束惩罚项,更新并优化目标函数得到新的迭代方程;最后,通过多次迭代,完成对肝脏CT图像的分割。实验结果表明,SFCM算法分割具有灰度不均匀和边界粘连的肝脏CT图像时得到的肝脏轮廓形状更加规则,准确率达到92.8%,比FCM和直觉模糊C均值（IFCM）算法的分割准确率分别提升了2.3和4.3个百分点,过分割率分别降低了4.9和5.3个百分点。     

Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model

Triplet Markov fields (TMF) model proposed recently is suitable for nonstationary image segmentation. For synthetic aperture radar (SAR) image segmentation, TMF model can adopt diverse statistical models for SAR data related to diverse radar backscattering sources. However, TMF model does not take into account the inherent imprecision associated with SAR images. In this paper, we propose a statistical fuzzy TMF (FTMF) model, which is a fuzzy clustering type treatment of TMF model, for unsupervised multi-class segmentation of SAR images. This paper contributes to SAR image segmentation in four aspects: (1) Nonstationarity of the statistical distribution of SAR intensity/amplitude data is taken into account to improve the spatial modeling capability of fuzzy TMF model. (2) Mean field theory is generalized to deal with planar variables to derive prior probability in fuzzy TMF model, which resolves the problem in Gibbs sampler in terms of computation cost. (3) A fuzzy objective function with regularization by Kullback–Leibler information of fuzzy TMF model is constructed for SAR image segmentation. The introduction of fuzziness for the belongingness of SAR image pixel makes fuzzy TMF model be able to retain more information from SAR image. (4) Fuzzy iterative conditional estimation (ICE) method, as an extension of the general ICE method is proposed to perform the model parameters estimation. The effectiveness of the proposed algorithm is demonstrated by application to simulated data and real SAR images.     

A reinforcement agent for object segmentation in ultrasound images

The principal contribution of this work is to design a general framework for an intelligent system to extract one object of interest from ultrasound images. This system is based on reinforcement learning. The input image is divided into several sub-images, and the proposed system finds the appropriate local values for each of them so that it can extract the object of interest. The agent uses some images and their ground-truth (manually segmented) version to learn from. A reward function is employed to measure the similarities between the output and the manually segmented images, and to provide feedback to the agent. The information obtained can be used as valuable knowledge stored in the Q-matrix. The agent can then use this knowledge for new input images. The experimental results for prostate segmentation in trans-rectal ultrasound images show high potential of this approach in the field of ultrasound image segmentation.     

A new iris segmentation method for non-ideal iris images

Many researchers have studied iris recognition techniques in unconstrained environments, where the probability of acquiring non-ideal iris images is very high due to off-angles, noise, blurring and occlusion by eyelashes, eyelids, glasses, and hair. Although there have been many iris segmentation methods, most focus primarily on the accurate detection with iris images which are captured in a closely controlled environment. This paper proposes a new iris segmentation method that can be used to accurately extract iris regions from non-ideal quality iris images. This research has following three novelties compared to previous works; firstly, the proposed method uses AdaBoost eye detection in order to compensate for the iris detection error caused by the two circular edge detection operations; secondly, it uses a color segmentation technique for detecting obstructions by the ghosting effects of visible light; and thirdly, if there is no extracted corneal specular reflection in the detected pupil and iris regions, the captured iris image is determined as a “closed eye” image.     

A multiobjective spatial fuzzy clustering algorithm for image segmentation

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, fuzzy c-means, two fuzzy c-means clustering algorithms with spatial information and a multiobjective variable string length genetic fuzzy clustering algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation.