A semi-supervised fuzzy clustering algorithm applied to gene expression data

Over the last decade there has been an increasing interest in semi-supervised clustering. Several studies have suggested that even a small amount of supervised information can significantly improve the results of unsupervised learning. One popular method of incorporating partial supervised information is through pair-wise constraints indicating whether a certain pair of patterns should belong to the same (Must-link) or different (Dont-link) clusters. In this study we propose a novel semi-supervised fuzzy clustering algorithm (SSFCA). The supervised information is incorporated via a method quantifying Must-link and/or Dont-link constraints. Additionally, we present an extension of SSFCA that allows the algorithm to automatically detect the number of clusters in the data. We apply SSFCA to the intrinsic problem of gene expression profiles clustering. The advantageous properties of fuzzy logic, inherited to SSFCA, allow genes to belong to more than one group, revealing this way more profound information concerning their multiple functioning roles. Finally, we investigate the incorporation of prior biological knowledge arriving from Gene Ontology in the process of selecting pair-wise constraints. Simulations on artificial and real life datasets proved that the proposed SSFCA significantly outperformed other standard and semi-supervised clustering methods.     

A fuzzy algorithm for color quantization of images

In this paper, we review a number of techniques for fuzzy color quantization. We show that the fuzzy membership paradigm is particularly suited to color quantization, where color cluster boundaries are not well defined. We propose a new fuzzy color quantization technique which incorporates a term for partition index. This algorithm produces better results than fuzzy C-means at a reduced computational cost. We test the results of the fuzzy algorithms using quality metrics which model the perception of the human visual system and illustrate that substantial quality improvements are achieved.     

A study on semi-supervised FCM algorithm

Most variants of fuzzy c-means (FCM) clustering algorithms involving prior knowledge are generally based on the modification of the objective function or the clustering process. This paper proposes a new weighted semi-supervised FCM algorithm (SSFCM-HPR) that transforms the prior knowledge in the labeled samples into constraint conditions in terms of fuzzy membership degrees, assigns different weights according to the representativeness of the samples, and then uses the HPR multiplier to solve the clustering problem. The “representativeness” of the labeled samples is decided by their distances to the cluster centers they belong to. In this paper, we take the ratio of the largest to the second largest fuzzy membership degree from a labeled sample as its weight. This algorithm not only retains the fuzzy partition of the labeled samples, which guarantees the effective guidance on the clustering process, but also can detect whether a sample is an outlier or not. Moreover, when part of the supervised information of the labeled samples is wrong, this algorithm can reduce the influence of the incorrectly labeled samples on the final clustering results. The experimental evaluation on synthetic and real data sets demonstrates the efficiency and effectiveness of our approach.     

一种基于ROI和多描述量化的图像传输方法

随着科学技术的进步和发展,图像已经成为了信息的一个重要来源,而图像中的重要信息往往只集中在部分区域。而且图像数据在信道中传输时,可能会发生数据包丢失或出错的问题。鉴于以上问题,在对图像ROI和多描述量化编码进行分析和综合的基础上,本文提出了一种结合图像感兴趣区域（ROI）提取和多描述量化,零树编码的图像传输方法。根据图像特征选定ROI区域,然后对ROI进行多描述量化编码,对其他区域进行普通的量化编码,试验表明此方法可以得到更好的重构图像。     

A semi-supervised privacy-preserving clustering algorithm for healthcare

With the proliferation of healthcare data, the cloud mining technology for E-health services and applications has become a hot research topic. While on the other hand, these rapidly evolving cloud mining technologies and their deployment in healthcare systems also pose potential threats to patient’s data privacy. In order to solve the privacy problem in the cloud mining technique, this paper proposes a semi-supervised privacy-preserving clustering algorithm. By employing a small amount of supervised information, the method first learns a Large Margin Nearest Cluster metric using convex optimization. Then according to the trained metric, the method imposes multiplicative perturbation on the original data, which can change the distribution shape of the original data and thus protect the privacy information as well as ensuring high data usability. The experimental results on the brain fiber dataset provided by the 2009 PBC demonstrated that the proposed method could not only protect data privacy towards secure attacks, but improve the clustering purity.     

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Segmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach.     

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。     

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.     

半监督学习算法在农用地分等中的应用

为了提高了土地评价模型的简易性、可解释性和准确性,以及克服传统土地评价模型中认为因素多的影响,提出利用关联规则挖掘算法从已知类别的训练样本提取其中的分类关联规则作为监督信息,结合非监督学习方法中的K-mean聚类算法,对大量未标定样本进行分类的半监督学习方法.该方法实现过程简单,分类准确率高,可推广性较强.对广东省土地资源的评价实验表明,利用半监督学习算法可得到较高的土地评价准确率94.0622%.     

A local semi-supervised Sammon algorithm for textual data visualization

Sammon’s mapping is a powerful non-linear technique that allow us to visualize high dimensional object relationships. It has been applied to a broad range of practical problems and particularly to the visualization of the semantic relations among terms in textual databases. The word maps generated by the Sammon mapping suffer from a low discriminant power due to the well known “curse of dimensionality” and to the unsupervised nature of the algorithm. Fortunately the textual databases provide frequently a manually created classification for a subset of documents that may help to overcome this problem. In this paper we first introduce a modification of the Sammon mapping (SSammon) that enhances the local topology reducing the sensibility to the ’curse of dimensionality’. Next a semi-supervised version is proposed that takes advantage of the a priori categorization of a subset of documents to improve the discriminant power of the word maps generated. The new algorithm has been applied to the challenging problem of word map generation. The experimental results suggest that the new model improves significantly well known unsupervised alternatives.

Using roadmaps to classify regions of space for autonomous robot navigation

The roadmap approach to robot path planning is one of the earliest methods. Since then, many different algorithms for building roadmaps have been proposed and widely implemented in mobile robots but their use has always been limited to planning in static, totally known environments. In this paper we combine the use of dynamic analogical representations of the environment with an efficient roadmap extraction method, to guide the robot navigation and to classify the different regions of space in which the robot moves. The paper presents the general reference architecture for the robotic system and then focuses on the algorithms for the construction of the roadmap, the classification of the regions of space and their use in robot navigation. Experimental results indicate the applicability and robustness of this approach in real situations.     

A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation

Dental X-ray image segmentation (DXIS) is an indispensable process in practical dentistry for diagnosis of periodontitis diseases from an X-ray image. It has been said that DXIS is one of the most important and necessary steps to analyze dental images in order to get valuable information for medical diagnosis support systems and other recognition tools. Specialized data mining methods for DXIS have been investigated to achieve high accuracy of segmentation. However, traditional image processing and clustering algorithms often meet challenges in determining parameters or common boundaries of teeth samples. It was shown that performance of a clustering algorithm is enhanced when additional information provided by users is attached to inputs of the algorithm. In this paper, we propose a new cooperative scheme that applies semi-supervised fuzzy clustering algorithms to DXIS. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Then, the FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Finally, Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is opted to clarify and improve the results based on the optimal result from the previous clustering method. The proposed framework is evaluated on a real collection of dental X-ray image datasets from Hanoi Medical University, Vietnam. Experimental results have revealed that clustering quality of the cooperative framework is better than those of the relevant ones. The findings of this paper have great impact and significance to researches in the fields of medical science and expert systems. It has been the fact that medical diagnosis is often an experienced and case-based process which requests long time practicing in real patients. In many situations, young clinicians do not have chance for such the practice so that it is necessary to utilize a computerized medical diagnosis system which could simulate medical processes from previous real evidences. By learning from those cases, clinicians would improve their experience and responses for later ones. In the view of expert systems, this paper made uses of knowledge-based algorithms for a practical application. This shows the advantages of such the algorithm in the conjunction domain between expert systems and medical informatics. The findings also suggested the most appropriate configuration of the algorithm and parameters for this problem that could be reused by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.     

A topology preserving non-rigid registration algorithm with integration shape knowledge to segment brain subcortical structures from MRI images

A new non-rigid registration method combining image intensity and a priori shape knowledge of the objects in the image is proposed. This method, based on optical flow theory, uses a topology correction strategy to prevent topological changes of the deformed objects and the a priori shape knowledge to keep the object shapes during the deformation process. Advantages of the method over classical intensity based non-rigid registration are that it can improve the registration precision with the a priori knowledge and allows to segment objects at the same time, especially efficient in the case of segmenting adjacent objects having similar intensities. The proposed algorithm is applied to segment brain subcortical structures from 15 real brain MRI images and evaluated by comparing with ground truths. The obtained results show the efficiency and robustness of our method.     

基于模糊距离变换的岩心图像颗粒分割算法

岩心CT图像中相邻颗粒之间存在相互粘连,粒度参数分析等需要对粘连颗粒进行分割。结合岩心粘连颗粒的特性,提出一种基于模糊距离变换的改进颗粒分割算法。首先,对预处理后的岩心图像进行模糊距离变换并提取出距离信息的灰度图像,然后利用形态学膨胀重构方法提取标记作为后续分水岭算法的种子点,根据种子点采用一种基于测地重建的改进分水岭算法得到相邻种子点之间的分割线,最后完成粘连颗粒的分割。实验结果表明本文算法可以提高粘连颗粒分割的准确度并减轻分水岭算法的过分割现象。     

Using a modified counter-propagation algorithm to classify conjoined data

Conjoined data is data in which the classes abut but do not overlap. It is difficult to determine the boundary between the classes, as there are no inherent clusters. As a result traditional classification methods, such as Counter-Propagation networks, may underperform. This paper describes a modified Counter-Propagation network that is able to refine the boundary definition and so perform better when classifying conjoined data. The efficiency with which network resources are used suggests that it is worthy of consideration for classifying all kinds of data, not just conjoined data.     

A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation

Dental X-ray image segmentation has an important role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoretically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solutions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and compared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.     

基于模糊聚类分析和HVS的自适应彩色图像水印算法

结合模糊C均值聚类算法和人眼视觉特性,提出了一种新的自适应彩色图像水印算法。首先,将彩色图像经模糊聚类分析,选取出适合于水印嵌入的位置;然后,分别在R、G、B 3个通道中利用小波域的视觉掩蔽特性自适应地修改水印嵌入强度,提取时,不需要用到原始图像。实验结果证明,含水印的彩色图像没有出现任何可感知的视觉失真,同时,该算法对一定的图像处理操作具有较强的鲁棒性。     

A fuzzy classifier with ellipsoidal regions

In this paper, we discuss a fuzzy classifier with ellipsoidal regions which has a learning capability. First, we divide the training data for each class into several clusters. Then, for each cluster, we define a fuzzy rule with an ellipsoidal region around a cluster center. Using the training data for each cluster, we calculate the center and the covariance matrix of the ellipsoidal region for the cluster. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. We evaluate our method using the Fisher iris data, numeral data of vehicle license plates, thyroid data, and blood cell data. The recognition rates (except for the thyroid data) of our classifier are comparable to the maximum recognition rates of the multilayered neural network classifier and the training times (except for the iris data) are two to three orders of magnitude shorter     

Characteristics of interest and fuzzy subsets

This paper considers the assessment of the relative extent to which a given member of a designated set of objects exhibits a characteristic of interest which is determined qualitatively through pairwise comparisons between the objects in question. While similarities between the concepts of characteristic and fuzzy subset are noted, the differences between them are stressed.