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Multiresolution Fuzzy C-Means Clustering Using Markov Random Field for Image Segmentation
Xuchao Li,Suxuan Bian
International Journal of Information Technology and Computer Science , 2009,
Abstract: In this paper, an unsupervised multiresolution image segmentation algorithm is put forward, which combines interscale and intrascale Markov random field and fuzzy c-means clustering with spatial constraints. In the initial label determination of wavelet coefficient phase, the statistical distribution property of wavelet coefficients is characterized by Gaussian mixture model, the properties of intrascale clustering and interscale persistence of wavelet coefficients are captured by Markov prior probability model. According to maximum a posterior rule, the initial label of wavelet coefficient from coarse to fine scale is determined. In the image segmentation phase, in order to overcome the shortcomings of conventional fuzzy c-means clustering, such as being sensitive to noise and lacking of spatial constraints, we construct the novel fuzzy c-means objective function based on the property of intrascale clustering and interscale persistence of wavelet coefficients, taking advantage of Lagrange multipliers, the improved objective function with spatial constraints is optimized, the final label of wavelet coefficient is determined by iteratively updating the membership degree and cluster centers. The experimental results on real magnetic resonance image and peppers image with noise show that the proposed algorithm obtains much better segmentation results, such as accurately differentiating different regions and being immune to noise.
Expectation maximization method for parameter estimation of image statistical model
图像统计模型参数估计中的期望最大值算法

Li Xuchao,
李旭超

中国图象图形学报 , 2012,
Abstract: Expectation maximization (EM)algorithm for parameter estimation of image statistical model is one of the striking research fields in recent decades.Based on the analysis of the EM algorithm,combining the current application research in parameter estimation of image statistical model,analysis and comparison are conducted in terms of the three improvement schemes of standard EM algorithm.In this paper,integrating image restoration,segmentation,object tracking and the fusion of other evolution optimization algorithms,through three aspects,such as the selection of missing data sets,the statistical model establishments of missing and incomplete data sets,and parameter estimation of image statistical models,as well as the advantages and disadvantages of the corresponding EM algorithm are exponded.The structure and complexity of EM algorithm,so far as to success or failure,are directly determined by the selection of missing data and the expression form of incomplete data.In the end,challenges and possible trends are discussed,and extensive applications of EM algorithm to parameter estimation of statistical model with missing data are pointed out.
The survey of fuzzy clustering method for image segmentation
图像分割中的模糊聚类方法

Li Xuchao,Liu Haikuan,Wang Fei,Bai Chunyan,
李旭超
,刘海宽,王飞,白春艳

中国图象图形学报 , 2012,
Abstract: The fuzzy c-means (FCM) clustering algorithm for image segmentation is one of the striking research fields in recent decades.Based on the analysis of the FCM algorithm,we combine the current application research in image segmentation,and we analyze and compare it in terms of measuring the expressions of the FCM algorithm.In this paper,through three aspects,such as single-resolution,multi-resolution,and the integration of other algorithms,the advantages and disadvantages of the improved FCM algorithms are expounded.In the end,some challenges and possible trends are discussed.
Performance Comparison between Rapid Sequencing Platforms for Ultra-Low Coverage Sequencing Strategy
Shengpei Chen, Sheng Li, Weiwei Xie, Xuchao Li, Chunlei Zhang, Haojun Jiang, Jing Zheng, Xiaoyu Pan, Hancheng Zheng, Jia Sophie Liu, Yongqiang Deng, Fang Chen, Hui Jiang
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0092192
Abstract: Ultra-low coverage sequencing (ULCS) is one of the most promising strategies for sequencing based clinical application. These clinical applications, especially prenatal diagnosis, have a strict requirement of turn-around-time; therefore, the application of ULCS is restricted by current high throughput sequencing platforms. Recently, the emergence of rapid sequencing platforms, such as MiSeq and Ion Proton, brings ULCS strategy into a new era. The comparison of their performance could shed lights on their potential application in large-scale clinic trials. In this study, we performed ULCS (<0.1X coverage) on both MiSeq and Ion Proton platforms for 18 spontaneous abortion fetuses carrying aneuploidy and compared their performance on different levels. Overall basic data and GC bias showed no significant difference between these two platforms. We also found the sex and aneuploidy detection indicated 100% sensitivity and 100% specificity on both platforms. Our study generated essential data from these two rapid sequencing platforms, which provides useful reference for later research and potentially accelerates the clinical applications of ULCS.
Salivary MicroRNAs as Promising Biomarkers for Detection of Esophageal Cancer
Zijun Xie, Gang Chen, Xuchao Zhang, Dongfeng Li, Jian Huang, Cuiqin Yang, Pingyong Zhang, Yuxuan Qin, Yifan Duan, Bo Gong, Zijun Li
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0057502
Abstract: Background and Purpose Tissue microRNAs (miRNAs) can detect cancers and predict prognosis. Several recent studies reported that tissue, plasma, and saliva miRNAs share similar expression profiles. In this study, we investigated the discriminatory power of salivary miRNAs (including whole saliva and saliva supernatant) for detection of esophageal cancer. Materials and Methods By Agilent microarray, six deregulated miRNAs from whole saliva samples from seven patients with esophageal cancer and three healthy controls were selected. The six selected miRNAs were subjected to validation of their expression levels by RT-qPCR using both whole saliva and saliva supernatant samples from an independent set of 39 patients with esophageal cancer and 19 healthy controls. Results Six miRNAs (miR-10b*, miR-144, miR-21, miR-451, miR-486-5p, and miR-634) were identified as targets by Agilent microarray. After validation by RT-qPCR, miR-10b*, miR-144, and miR-451 in whole saliva and miR-10b*, miR-144, miR-21, and miR-451 in saliva supernatant were significantly upregulated in patients, with sensitivities of 89.7, 92.3, 84.6, 79.5, 43.6, 89.7, and 51.3% and specificities of 57.9, 47.4, 57.9%, 57.9, 89.5, 47.4, and 84.2%, respectively. Conclusions We found distinctive miRNAs for esophageal cancer in both whole saliva and saliva supernatant. These miRNAs possess discriminatory power for detection of esophageal cancer. Because saliva collection is noninvasive and convenient, salivary miRNAs show great promise as biomarkers for detection of esophageal cancer in areas at high risk.
An Optimal Control Approach to the Multi-Agent Persistent Monitoring Problem in Two-Dimensional Spaces
Xuchao Lin,Christos G. Cassandras
Mathematics , 2013,
Abstract: We address the persistent monitoring problem in two-dimensional mission spaces where the objective is to control the trajectories of multiple cooperating agents to minimize an uncertainty metric. In a one-dimensional mission space, we have shown that the optimal solution is for each agent to move at maximal speed and switch direction at specific points, possibly waiting some time at each such point before switching. In a two-dimensional mission space, such simple solutions can no longer be derived. An alternative is to optimally assign each agent a linear trajectory, motivated by the one-dimensional analysis. We prove, however, that elliptical trajectories outperform linear ones. With this motivation, we formulate a parametric optimization problem in which we seek to determine such trajectories. We show that the problem can be solved using Infinitesimal Perturbation Analysis (IPA) to obtain performance gradients on line and obtain a complete and scalable solution. Since the solutions obtained are generally locally optimal, we incorporate a stochastic comparison algorithm for deriving globally optimal elliptical trajectories. Numerical examples are included to illustrate the main result, allow for uncertainties modeled as stochastic processes, and compare our proposed scalable approach to trajectories obtained through off-line computationally intensive solutions.
Noninvasive Prenatal Detection for Pathogenic CNVs: The Application in α-Thalassemia
Huijuan Ge, Xuan Huang, Xuchao Li, Shengpei Chen, Jing Zheng, Haojun Jiang, Chunlei Zhang, Xiaoyu Pan, Jing Guo, Fang Chen, Ning Chen, Qun Fang, Hui Jiang, Wei Wang
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0067464
Abstract: Background The discovery of cell free fetal DNA (cff-DNA) in maternal plasma has brought new insight for noninvasive prenatal diagnosis. Combining with the rapidly developed massively parallel sequencing technology, noninvasive prenatal detection of chromosome aneuploidy and single base variation has been successfully validated. However, few studies discussed the possibility of noninvasive pathogenic CNVs detection. Methodology/Principal Findings A novel algorithm for noninvasive prenatal detection of fetal pathogenic CNVs was firstly tested in 5 pairs of parents with heterozygote α-thalassemia of Southeast Asian (SEA) deletion using target region capture sequencing for maternal plasma. Capture probes were designed for α-globin (HBA) and β-globin (HBB) gene, as well as 4,525 SNPs selected from 22 automatic chromosomes. Mixed adaptors with 384 different barcodes were employed to construct maternal plasma DNA library for massively parallel sequencing. The signal of fetal CNVs was calculated using the relative copy ratio (RCR) of maternal plasma combined with the analysis of R-score and L-score by comparing with normal control. With mean of 101.93× maternal plasma sequencing depth for the target region, the RCR value combined with further R-score and L-score analysis showed a possible homozygous deletion in the HBA gene region for one fetus, heterozygous deletion for two fetus and normal for the other two fetus, which was consistent with that of invasive prenatal diagnosis. Conclusions/Significance Our study showed the feasibility to detect pathogenic CNVs using target region capture sequencing, which might greatly extend the scope of noninvasive prenatal diagnosis.
A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing
Chunlei Zhang, Chunsheng Zhang, Shengpei Chen, Xuyang Yin, Xiaoyu Pan, Ge Lin, Yueqiu Tan, Ke Tan, Zhengfeng Xu, Ping Hu, Xuchao Li, Fang Chen, Xun Xu, Yingrui Li, Xiuqing Zhang, Hui Jiang, Wei Wang
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0054236
Abstract: Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4~9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.
Prenatal Detection of Aneuploidy and Imbalanced Chromosomal Arrangements by Massively Parallel Sequencing
Shan Dan, Fang Chen, Kwong Wai Choy, Fuman Jiang, Jingrong Lin, Zhaoling Xuan, Wei Wang, Shengpei Chen, Xuchao Li, Hui Jiang, Tak Yeung Leung, Tze Kin Lau, Yue Su, Weiyuan Zhang, Xiuqing Zhang
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0027835
Abstract: Fetal chromosomal abnormalities are the most common reasons for invasive prenatal testing. Currently, G-band karyotyping and several molecular genetic methods have been established for diagnosis of chromosomal abnormalities. Although these testing methods are highly reliable, the major limitation remains restricted resolutions or can only achieve limited coverage on the human genome at one time. The massively parallel sequencing (MPS) technologies which can reach single base pair resolution allows detection of genome-wide intragenic deletions and duplication challenging karyotyping and microarrays as the tool for prenatal diagnosis. Here we reported a novel and robust MPS-based method to detect aneuploidy and imbalanced chromosomal arrangements in amniotic fluid (AF) samples. We sequenced 62 AF samples on Illumina GAIIx platform and with averagely 0.01× whole genome sequencing data we detected 13 samples with numerical chromosomal abnormalities by z-test. With up to 2× whole genome sequencing data we were able to detect microdeletion/microduplication (ranged from 1.4 Mb to 37.3 Mb of 5 samples from chorionic villus sampling (CVS) using SeqSeq algorithm. Our work demonstrated MPS is a robust and accurate approach to detect aneuploidy and imbalanced chromosomal arrangements in prenatal samples.
PSCC: Sensitive and Reliable Population-Scale Copy Number Variation Detection Method Based on Low Coverage Sequencing
Xuchao Li, Shengpei Chen, Weiwei Xie, Ida Vogel, Kwong Wai Choy, Fang Chen, Rikke Christensen, Chunlei Zhang, Huijuan Ge, Haojun Jiang, Chang Yu, Fang Huang, Wei Wang, Hui Jiang, Xiuqing Zhang
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0085096
Abstract: Background Copy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method. Methodology/Principal Findings In our novel method, two-step correction was used to remove biases caused by local GC content and complex genomic characteristics. We chose a binary segmentation method to locate CNV segments and designed combined statistics tests to ensure the stable performance of the false positive control. The simulation data showed that our PSCC method could achieve 99.7%/100% and 98.6%/100% sensitivity and specificity for over 300 kb CNV calling in the condition of LCS (~2×) and ultra LCS (~0.2×), respectively. Finally, we applied this novel method to analyze 34 clinical samples with an average of 2× LCS. In the final results, all the 31 pathogenic CNVs identified by aCGH were successfully detected. In addition, the performance comparison revealed that our method had significant advantages over existing methods using ultra LCS. Conclusions/Significance Our study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.
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