Binary Segmentation Method Research Articles

Characterization investigation on pore-resistance relationship of oil contaminants in soil porous structure

The leaked contaminants from buried oil pipelines have a great potential to damage the ecological environment. It is significant to accurately and effectively predict the migration destination and diffusion range of oil contaminants in soil, which can keep contaminants to a minimum, and also can help to formulate the environmental impact assessment and remediation plan. In this paper, the Quartet Structure Generation Set algorithm (QSGS) is adopted to reconstruct the soil porous structure, and the binary image segmentation method is introduced to evaluate the algorithm quantitatively. The D2Q9 scheme of Lattice Boltzmann Method (LBM) is employed to simulate the mesoscopic scale migration of oil contaminants in soil. The influence of different porosities on the velocity field, pressure field and streamline distribution of oil contaminants in the computational domain is analyzed. Based on the measurement experiment of resistance coefficient in porous medium, the linear relationship between the square root of pressure difference Δp0.5 and the percolation velocity v is obtained under four conditions. The migration resistance coefficient κR is introduced to characterize the migration ability of fluid in porous medium. When the contaminants type and permeation path length of fluid in porous medium are fixed, the migration resistance coefficient κR decreases with the increase of soil porosity, which can be described with a quadratic polynomial function. Thus, it is essential to investigate of pore-resistance relationship for the mesoscopic migration of oil contaminants in soil.

Copy Number Variation Detection Using Total Variation.

Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For high-throughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.

Extraction of residential building instances in suburban areas from mobile LiDAR data

In the recent years, mobile LiDAR data has become an important data source for building mapping. However, it is challenging to extract building instances in residential areas where buildings of different structures are closely distributed and surrounded by cluttered objects such as vegetations. In this paper, we present a new “localization then segmentation” framework to tackle these problems. First, a hypothesis and selection method is proposed to localize buildings. Rectangle proposals which indicate building locations are generated using projections of vertical walls obtained by region growing. The selection of rectangles is formulated as a constrained maximization problem, which is solved by linear programming. Then, point clouds are divided into groups, each of which contains one building instance. A foreground-background segmentation method is then proposed to extract buildings from complex surroundings in each group. Based on the graph of points, an objective function which integrates local geometric features and shape priors is minimized by the graph cuts. The experiments are conducted in two large and complex scenes, Calgary and Kentucky residential areas. The completeness and correctness of building localization in the former dataset are 87.2% and 91.34%, respectively. In the latter dataset, the completeness and correctness of building localization are 100% and 96.3%, respectively. Based on the tests, our binary segmentation method outperforms existing methods regarding the F1 measure. These results demonstrate the feasibility and effectiveness of our framework in extracting instance-level residential buildings from mobile LiDAR point clouds in suburban areas.

Efficient 3D Volume Reconstruction from a Point Cloud Using a Phase-Field Method

We propose an explicit hybrid numerical method for the efficient 3D volume reconstruction from unorganized point clouds using a phase-field method. The proposed three-dimensional volume reconstruction algorithm is based on the 3D binary image segmentation method. First, we define a narrow band domain embedding the unorganized point cloud and an edge indicating function. Second, we define a good initial phase-field function which speeds up the computation significantly. Third, we use a recently developed explicit hybrid numerical method for solving the three-dimensional image segmentation model to obtain efficient volume reconstruction from point cloud data. In order to demonstrate the practical applicability of the proposed method, we perform various numerical experiments.

Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering

The change in the DNA is a form of genetic variation in the human genome. In addition, the DNA copy number change is also linked with the progression of many emerging diseases. Array-based Comparative Genomic Hybridization (CGH) is considered as a major task when measuring the DNA copy number change across the genome. Moreover, DNA copy number change is an essential measure to diagnose the cancer disease. Next generation sequencing is an important method for studying the spread of infectious disease qualitatively and quantitatively. CGH is widely used in continuous monitoring of copy number of thousands of genes throughout the genome. In recent years, the size of the DNA sequence data is very large. Hence, there is a need to use a scalable machine learning approach to overcome the various issues in DNA copy number change detection. In this paper, we use a Bayesian hidden Markov model (HMM) with Gaussian Mixture (GM) Clustering approach to model the DNA copy number change across the genome. The proposed Bayesian HMM with GM Clustering approach is compared with various existing approaches such as Pruned Exact Linear Time method, binary segmentation method and segment neighborhood method. Experimental results demonstrate the effectiveness of our proposed change detection algorithm.

Spatial cumulative sum algorithm with big data analytics for climate change detection

Big data plays a vital role in the prediction of diseases that occur due to climate change. For such predictions, scalable data storage platforms and efficient change detection algorithms are required to monitor the climate change. However, traditional data storage techniques and algorithms are not applicable to process the huge amount of climate data. This paper presents a scalable data processing framework with a novel change detection algorithm. The large volume of climate data is stored on Hadoop Distributed File System (HDFS) and MapReduce algorithm is applied to calculate the seasonal average of climate parameters. Spatial autocorrelation based climate change detection algorithm is proposed in this paper to monitor the changes in the seasonal climate. The proposed climate change detection algorithm is compared with various existing approaches such as pruned exact linear time method, binary segmentation method, and segment neighborhood method.

Reverse-Projection Method for Measuring Camera MTF

This paper proposes several adjustments to the ISO 12233 slanted edge algorithm for estimating camera MTF. First, the Ridler-Calvard binary image segmentation method is used to find the line. Secondly, total least squares, rather than ordinary least squares, is used to compute the line parameters. Finally, the pixel values are projected in the reverse direction from the 1D array to the 2D image, rather than from the 2D image to the 1D array. Together, these changes yield an algorithm that exhibits significantly less variation than existing techniques when applied to real images. In particular, the proposed algorithm is largely invariant to the rotation angle of the edge as well as to the size of the image crop.

Multiple change-point detection for non-stationary time series using Wild Binary Segmentation

We propose a new technique for consistent estimation of the number and locations of the change-points in the second-order structure of a time series. The core of the segmentation procedure is the Wild Binary Segmentation method(WBS), a technique which involves a certain randomised mechanism. The advantage of WBS over the standard Binary Segmentation lies in its localisation feature, thanks to which it works in cases where the spacings between change-points are short. In addition, we do not restrict the total number of change-points a time series can have. We also ameliorate the performance of our method by combining the CUSUM statistics obtained at different scales of the wavelet periodogram, our main change-point detection statistic, which allows a rigorous estimation of the local autocovariance of a piecewise-stationary process. We provide a simulation study to examine the performance of our method for different types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package wbsts, available from CRAN.

A multiphase segmentation method based on binary segmentation method for Gaussian noisy image

In this paper, a new multiphase segmentation method is proposed, in which a binary segmentation method is used in an iterative process. At each time of iteration, the region of pixels with darker mean value of intensity is separated from other regions. This segmentation is done by means of intensity function, eigenvector of Hessian matrix, and Curvelet. Proper extraction of the pixels around and on the ridge in the Gaussian noisy image and simultaneous denoizing and segmentation are the advantages of the present method. Experimental results are provided for showing the efficiency of our method.

In-frame and inter-frame information based infrared moving small target detection under complex cloud backgrounds

Infrared moving small target detection under complex cloud backgrounds is one of the key techniques of infrared search and track (IRST) systems. This paper proposes a novel method based on in-frame inter-frame information to detect infrared moving small targets accurately. For a single frame, in the spatial domain, a directional max-median filter is developed to make a pre-processing and a background suppression filtering template is utilized on the denoised image to highlight target. Then, targets in cloud regions and non-cloud regions are extracted by different thresholds according to a cloud discrimination method so that a spatial domain map (SDM) is acquired. In the frequency domain, we design an α-DoB band-pass filter to conduct coarse saliency detection and make an amplitude transformation with smoothing processing which is the so-called elaborate saliency detection. Furthermore, a frequency domain map (FDM) is acquired by an adaptive binary segmentation method. Lastly, candidate targets in single frame are extracted by a discrimination based on intensity and spatial distance criteria. For consecutive frames, a false alarm suppression is conducted on account of differences of motion features between moving target and false alarms to improve detection accuracy again. Large numbers of experiments demonstrate that the proposed method has satisfying detection effectiveness and robustness for infrared moving small target detection under complex cloud backgrounds.

Portfolio optimization of dynamic Copula models for dependent financial data using change point approach

In this paper, the portfolio optimization based on CV aR is performed using the dynamic copula model for financial data. Determining thebest model of dependency between financial data has an important role intaking appropriate investment decisions. Due to the financial data is alwaysağected by the *uctuations of the economic factors, the dynamic model washandled. On the other hand change point detection is also important for investment decisions. So this study presents an application of dynamic copulamodel with change point approach. We take the currency data (USD andEUR) from Turkish Central Bank to construct a portfolio. This study consists of two stages. In the first stage, the marginal distributions and copulamodels of currency data are defined for full sample, and the portfolio optimization based on CV aR is performed. In the second stage, the change periodsof copula models are determined using binary segmentation method, and the portfolio optimization based on CV aR is performed for each period

A Bayesian Analysis of Copy Number Variations in Array Comparative Genomic Hybridization Data

Array Comparative Genomic Hybridization (CGH) has been widely used for detecting genomic copy number variations (CNVs). The central goal of array CGH data analysis is to accurately detect homogeneous regions of log intensity ratios which represent relative changes in DNA copy number. Various methods have been proposed in recent years. Most methods, however, do not consider correlations of neighboring probe measurements, and are usually designed for analysis at single sample level rather than detecting common or recurrent CNVs among multiple samples. We propose a Bayesian segment-based approach for efficient analysis of array CGH data. The proposed method is based on simple assumptions but is general enough to accommodate various spatial correlations among probe measurements. It also allows for multiple samples with recurrent CNVs, therefore is able to borrow strength across samples. In contrast to another probe-based approach developed in the same Bayesian framework, the segment-based approach parameterizes the mean log intensity ratios in a more appropriate way, which leads to a posterior sampling scheme based on reversible-jump Markov chain Monte Carlo. We perform a simulation study to compare these two approaches and the commonly-used circular binary segmentation method and Bayesian hidden Markov model method. The segment-based approach achieves better estimation accuracy and higher computational efficiency compared to the probe-based approach, and also provides improved results compared to the other two methods, especially for data with relatively low signal to noise ratio and high correlation. The segment-based approach is further applied to the Corriel cell lines data and Pancreatic Adenocarcinoma data.

Modified screening and ranking algorithm for copy number variation detection.

Copy number variation (CNV) is a type of structural variation, usually defined as genomic segments that are 1 kb or larger, which present variable copy numbers when compared with a reference genome. The screening and ranking algorithm (SaRa) was recently proposed as an efficient approach for multiple change-points detection, which can be applied to CNV detection. However, some practical issues arise from application of SaRa to single nucleotide polymorphism data. In this study, we propose a modified SaRa on CNV detection to address these issues. First, we use the quantile normalization on the original intensities to guarantee that the normal mean model-based SaRa is a robust method. Second, a novel normal mixture model coupled with a modified Bayesian information criterion is proposed for candidate change-point selection and further clustering the potential CNV segments to copy number states. Simulations revealed that the modified SaRa became a robust method for identifying change-points and achieved better performance than the circular binary segmentation (CBS) method. By applying the modified SaRa to real data from the HapMap project, we illustrated its performance on detecting CNV segments. In conclusion, our modified SaRa method improves SaRa theoretically and numerically, for identifying CNVs with high-throughput genotyping data. The modSaRa package is implemented in R program and freely available at http://c2s2.yale.edu/software/modSaRa. Supplementary data are available at Bioinformatics online.

Is there an age cutoff to apply adult formulas for GFR estimation in children?

Estimation of glomerular filtration rate (eGFR) using a common formula for both adult and pediatric populations is challenging. Using inulin clearances (iGFRs), this study aims to investigate the existence of a precise age cutoff beyond which the Modification of Diet in Renal Disease (MDRD), the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), or the Cockroft-Gault (CG) formulas, can be applied with acceptable precision. Performance of the new Schwartz formula according to age is also evaluated. We compared 503 iGFRs for 503 children aged between 33 months and 18 years to eGFRs. To define the most precise age cutoff value for each formula, a circular binary segmentation method analyzing the formulas' bias values according to the children's ages was performed. Bias was defined by the difference between iGFRs and eGFRs. To validate the identified cutoff, 30% accuracy was calculated. For MDRD, CKD-EPI and CG, the best age cutoff was ≥14.3, ≥14.2 and ≤10.8 years, respectively. The lowest mean bias and highest accuracy were -17.11 and 64.7% for MDRD, 27.4 and 51% for CKD-EPI, and 8.31 and 77.2% for CG. The Schwartz formula showed the best performance below the age of 10.9 years. For the MDRD and CKD-EPI formulas, the mean bias values decreased with increasing child age and these formulas were more accurate beyond an age cutoff of 14.3 and 14.2 years, respectively. For the CG and Schwartz formulas, the lowest mean bias values and the best accuracies were below an age cutoff of 10.8 and 10.9 years, respectively. Nevertheless, the accuracies of the formulas were still below the National Kidney Foundation Kidney Disease Outcomes Quality Initiative target to be validated in these age groups and, therefore, none of these formulas can be used to estimate GFR in children and adolescent populations.

Copy number variation in Thai population.

Copy number variation (CNV) is a major genetic polymorphism contributing to genetic diversity and human evolution. Clinical application of CNVs for diagnostic purposes largely depends on sufficient population CNV data for accurate interpretation. CNVs from general population in currently available databases help classify CNVs of uncertain clinical significance, and benign CNVs. Earlier studies of CNV distribution in several populations worldwide showed that a significant fraction of CNVs are population specific. In this study, we characterized and analyzed CNVs in 3,017 unrelated Thai individuals genotyped with the Illumina Human610, Illumina HumanOmniexpress, or Illumina HapMap550v3 platform. We employed hidden Markov model and circular binary segmentation methods to identify CNVs, extracted 23,458 CNVs consistently identified by both algorithms, and cataloged these high confident CNVs into our publicly available Thai CNV database. Analysis of CNVs in the Thai population identified a median of eight autosomal CNVs per individual. Most CNVs (96.73%) did not overlap with any known chromosomal imbalance syndromes documented in the DECIPHER database. When compared with CNVs in the 11 HapMap3 populations, CNVs found in the Thai population shared several characteristics with CNVs characterized in HapMap3. Common CNVs in Thais had similar frequencies to those in the HapMap3 populations, and all high frequency CNVs (>20%) found in Thai individuals could also be identified in HapMap3. The majorities of CNVs discovered in the Thai population, however, were of low frequency, or uniquely identified in Thais. When performing hierarchical clustering using CNV frequencies, the CNV data were clustered into Africans, Europeans, and Asians, in line with the clustering performed with single nucleotide polymorphism (SNP) data. As CNV data are specific to origin of population, our population-specific reference database will serve as a valuable addition to the existing resources for the investigation of clinical significance of CNVs in Thais and related ethnicities.

Target Location of Industrial Robot Based on Vision

Vision is the most important way to obtain information from the word. This paper collected the target image using industrial robot vision system, and We get Black and white images using binary image segmentation method, then the contour of each object in the image can be obtained with edge detection and contour extraction, The centroid position was confirmed using minimum enclosing rectangle method after gaining the outline of target. The experimental results show that this method can quickly and accurately obtain multiple target centroid position.

PSCC: Sensitive and Reliable Population-Scale Copy Number Variation Detection Method Based on Low Coverage Sequencing

BackgroundCopy 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 FindingsIn 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/SignificanceOur study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.

Microarray analysis reveals abnormal chromosomal complements in over 70% of 14 normally developing human embryos

What are the aneuploidy rates and incidence of mosaicism in good-quality human preimplantation embryos. High-level mosaicism and structural aberrations are not restricted to arrested or poorly developing embryos but are also common in good-quality IVF embryos. Humans, compared with other mammals, have a poor fertility rate, and even IVF treatments have a relatively low success rate. It is known that human gametes and early preimplantation embryos carry chromosomal abnormalities that are thought to lower their developmental potential. The embryos studied came from nine young (age <35 years old) IVF patients and were part of a cohort of embryos that all resulted in healthy births. These 14 embryos inseminated by ICSI and cryopreserved on Day 2 of development were thawed, cultured overnight and allowed to succumb by being left at room temperature for 24 h. Following removal of the zona pellucida, blastomeres were disaggregated and collected. There were 91 single blastomeres collected and amplified by multiple displacement amplification. Array-comparative genomic hybridization was performed on the amplified DNA. Array-data were normalized and aneuploidy was detected by the circular binary segmentation method. The good-quality embryos exhibited high rates of aneuploidy, 10 of 14 (71.4%) of the embryos being mosaic. While none of the embryos had the same aneuploidy pattern in all cells, 4 of 14 (28.6%) were uniformly diploid. Of the 70 analysed blastomeres, 55.7% were diploid and 44.3% had chromosomal abnormalities, while 29% of the abnormal cells carried structural aberrations. Finding such a high rate of aneuploidy and mosaicism in excellent quality embryos from cycles with a high implantation rate warrants further research on the origin and significance of chromosomal abnormalities in human preimplantation embryos. This research was supported by the Instituut voor de aanmoediging van innovatie door Wetenschap en Technologie in Vlaanderen (IWT-Vlaanderen). A.M. is a PhD student at the IWT-Vlaanderen. C.S. is a postdoctoral fellow at the FWO Vlaanderen. There are no competing interests.

Consistent multiple testing for change points

The change point problem for independent normal means is considered as a multiple testing problem. Two stepwise methods are considered. Namely, the binary segmentation method of Vostrikova (1981) [7] and the maximum residual down method of Cohen et al. (2009) [5]. Both of these methods are shown to be consistent. Consistent here means that as sample sizes tend to infinity, the probability of making an error (false rejection or false acceptance) tends to zero.

Biological and Molecular Characterization by Integrative Genomics Approach of CMA-03/06, a Newly Established Interleukin-6 Independent Variant of the CMA-03 Human Myeloma Cell Line.

Abstract 1814Poster Board I-840 BackgroundThe growth and survival of multiple myeloma (MM) cells in the bone marrow microenvironment is regulated by functional complex interactions between the tumor cells and the surrounding bone marrow stromal cells mediated by adhesion molecules and the production of several cytokines of which interleukin-6 (IL-6) has been identified as the most important. Major advances in the investigation of MM biology were made possible by the availability of human myeloma cell lines (HMCLs). The IL-6-dependent CMA-03 cell line was established in our laboratory from a peritoneal effusion of a refractory relapsed MM patient. By gradually decreasing the IL-6 added to the culture, an IL-6-independent variant, CMA-03/06, could be obtained. Aims. To perform a biological and molecular characterization of this novel cell line, and to provide insights into the signaling pathways and target genes involved in the growth and survival of CMA-03/06. Methods. The growth, immunophenotypic, cytogenetic and fluorescence in situ hybridization (FISH) characterization of CMA-03/06 cell line was performed by means of standard procedures. IL-6 production into the culture media was determined using a high sensitivity IL-6 specific ELISA. Genome-wide profiling data were generated by means of Affymetrix GeneChip® Human Mapping 250K Nsp arrays; copy number (CN) alterations were calculated using the DNAcopy Bioconductor package, based on circular binary segmentation method. Global gene expression profiling (GEP) was performed by means of the GeneChip® Human Gene 1.0 ST Arrays (Affymetrix); the supervised analyses were done using the SAM software version 3.0. ResultsUnlike CMA-03, the addition of IL-6 to the culture medium of CMA-03/06 cells or co-culture with multipotent mesenchymal stromal cells did not induce an increase in CMA-03/06 proliferation. IL-6 was not detected in the supernatants from either CMA-03 or CMA-03/06 cell lines within 48 h, suggesting that the IL-6 independence of CMA03/06 cells is not a result of the development of an autocrine IL-6 loop. Nevertheless, IL-6 induced the activation of STAT3 and STAT1 in both cell lines, even if a slight constitutive STAT3 phosphorylation was found in CMA-03/06. The immunophenotypic analysis showed a significant difference in the expression of three antigens in the 2 cell lines: CD45 was considerably reduced in CMA-03/06 cells, whereas they were found positive for both chains of IL-6 receptor, CD126 and CD130, almost undetectable in CMA-03. Conventional cytogenetic and FISH analyses did not reveal differences between the 2 HMCLs. The genome-wide analysis allowed the identification of about 100 altered chromosomal regions common to both HMCLs, mostly DNA gains. Comparison of CMA-03/06 and CMA-03 cells evidenced a different CN in only 15 small chromosomal regions, 8 of which did not contain any transcript, whereas few genes were located on the other ones. GEP analysis of CMA-03/06 compared with CMA-03 identified 21 upregulated and 47 downregulated genes, many of which particularly relevant for MM biology, mainly involved in cellular signaling, cell cycle, cell adhesion, cell development, regulation of transcription, immunologic, inflammatory or defense activity, apoptosis. None of the genes differentially expressed in CMA-03/06 compared with CMA-03 except 1 were positioned on the chromosomal regions showing a different CN. Finally, CMA-03/06 cell line showed a lower susceptibility to camptothecin-induced apoptosis compared to CMA-03 cells. ConclusionsOur data show the IL-6 independence of CMA-03/06 cell line in the absence of an autocrine IL-6 loop; the cells, however, maintain the IL-6 signaling pathway responsiveness. A consistent number of genes particularly relevant for MM biology were found deregulated in CMA-03/06 cell line compared with CMA-03. Furthermore, CMA-03/06 cell line shows an increased resistance to apoptosis. The novel CMA03/06 cell line may thus represent a suitable model for studies investigating molecular mechanisms involved in clonal evolution towards IL-6 and/or stroma-independent growth and survival of myeloma cells. DisclosuresNo relevant conflicts of interest to declare.