15545048
ELB-Q: A New Method for Improving the Robustness in DNA Microarray Image Quantification
Reliable and robust quantification of signal intensities is a critical step in microarray-based biomedical studies. However, traditional techniques for microarray image processing would face significant challenges if the number of pixels used for the quantification of the local background and the foreground decreases dramatically. We have developed a new method, ELB-Q, which, by design, is well suited for the image quantification of microarrays with very high density of spot layout (large number of spots arranged in unit area). In ELB-Q, a large extended local background (ELB) inter spot region excluding those ldquonoise of the backgroundrdquo pixels is used for estimating the local background, and the quantification of spot intensities (mean and median) in the putative target spot regions is performed after further excluding background pixels in these areas based on the cutoff values established during the ELB calculation. ELB-Q takes advantage of the abundant spatial information around each spot of interest, makes no assumption of the shape and size of the spots, and needs no sophisticated adjustment. We show results of image processing using ELB-Q on both the simulated data and real DNA microarrays, which compare favorably in robustness and accuracy against those obtained with Gene Pix Pro 6.0 (Axon Instruments, 1999) and the Markov random field (MRF) modeling approach (O. Demirkaya , Bioinformatics, vol. 21, pp. 2994-3000, 2005). The ELB-Q software is developed in Matlab, and is available upon request.
29748731
Support vector machine networks for multi-class classification
The support vector machine (SVM) has recently attracted growing interest in pattern classification due to its competitive performance. It was originally designed for two-class classification, and many researchers have been working on extensions to multiclass. In this paper, we present a new framework that adapts the SVM with neural networks and analyze the source of misclassification in guiding our preprocessing for optimization in multiclass classification. We perform experiments on the ORL database and the results show that our framework can achieve high recognition rates.
207113548
A Hybrid Two-Phase Algorithm For Face Recognition
Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.
11423295
Multi-view face identification and pose estimation using B-spline interpolation
The available face views in the training set are mostly limited. In this paper, we present a view interpolation method using nonlinear B-spline on face manifolds. Two models, the inner-outer ellipse model and the moment of inertia model, are developed to estimate the pose orientation. We use the limited view-pose face images to form the pose eigen space. Then, based on these nonlinear manifolds we form a B-spline for each individual. Identification is to compute the shortest Euclidean distance from a given test view to the nearest point within one of these B-splines. Once the test view is classified as a familiar individual in the training set, not only can the individual be identified, but also the pose angle can be estimated. Experimental results show that B-spline interpolation can achieve a recognition rate of 95%.
1936453
A hierarchical refinement algorithm for fully automatic gridding in spotted DNA microarray image processing
Gridding, the first step in spotted DNA microarray image processing, usually requires human intervention to achieve acceptable accuracy. We present a new algorithm for automatic gridding based on hierarchical refinement to improve the efficiency, robustness and reproducibility of microarray data analysis. This algorithm employs morphological reconstruction along with global and local rotation detection, non-parametric optimal thresholding and local fine-tuning without any human intervention. Using synthetic data and real microarray images of different sizes and with different degrees of rotation of subarrays, we demonstrate that this algorithm can detect and compensate for alignment and rotation problems to obtain reliable and robust results.
16445417
Multiple Viewpoints Based Ontology Integration
Ontology integration is a focus on ontology application field. Ontology can be viewed as a kind of software product. Ontology integration needs to be directed by methodology. In many applications, we need to integrate existed ontologies for a unified ontology for application requirements. The ontology to be integrated can be viewed as a viewpoint of the unified ontology. A multiple-viewpoints-based ontology integration approach is introduced by multiple viewpoints theory in requirement engineering. We define ontology viewpoint by characters of ontology and use conceptual graph to represent semantic in ontology. We discuss the inconsistency checking inner ontology viewpoint and among ontology viewpoints. At last, we use a concept lattice to construct the concept hierarchy.
2401406
Graph Theory Application in Cell Nuleus Segmentation, Tracking and Identification
A novel cell nucleus tracking approach is raised in this paper, developed from applied graph theory that simultaneously handles major challenges of cells touching, splitting, disappearing and emerging. The key point of our work lies in the graphs with robust structures that constructed from large cells groups. Then the problem of nucleus tracking is simplified to that of vertex matching between graphs generated from successive frames. Single cells are modeled as vertices in the graph and edges are built between neighboring ones, through which cells in large quantities are connected altogether. The algorithm works through a process of graph saturation following vertices with maximal entering belief-propagation flows. Feasibility of our approach is validated by so-called 'Crowds Model' that explains motional features of multi-cells groups. In processing CT image sources with high cell intensities as well as complex variations in structures, robustness of our approach is confirmed in that both successful tracking rates and segmentation rates are kept above 96%.
5848822
Soft decoder architecture of LT codes
Luby transform (LT) codes, as the first class of efficient rateless codes, attract a lot of attention in the coding theory field. However, the VLSI implementation of LT codes is challenging due to its random code construction characteristic as well as the flexible output length. In this paper, we present an applicable architecture of a soft-decision LT decoder with a block length of 1024 bits and 100 iterations. Partly parallel input node processing and output node processing techniques are both adopted to accelerate decoding speed. An efficient router and reverse router are designed to indicate the graphic connectivity between input nodes and output nodes. The parallel architecture is prototyped on the target FPGA device.
225470
A 3D Self-Adjust Region Growing Method for Axon Extraction
Neuron axon analysis is an important means to investigate disease mechanisms and signaling pathways in neurobiology and often requires collecting a great amount of statistical information and phenomena. Automated extraction of axons in 3D microscopic images posts a key problem in the field of neuron axon analysis. To address tortuous axons in 3D volumes, a self-adjust region growing approach referring to surface modeling and self-adjustment which takes advantage of the nature of axon (e.g., continuity), is presented. Experimental results on axon volumes show that the proposed scheme provides a reliable solution to axon retrieving and overcomes several common drawbacks from other existing methods.
17637228
Research on the Application of Industrial CT for Relics Image Reconstruction
The purpose of this paper is to visualizing the internal structure of historic relics by using the industrial computed-tomography (CT). The image reconstruction of CT scan data can be used to observe and analyze the antique inside that the eye has no way to distinguish without disruption. This paper have built the simulated testing model, produced the data of simulating projection, and accomplished the comparison of image reconstruction between of ART(SIRT) and FDK algorithm. After handling the real data of different materials, the experiment and analysis of CT image reconstruction have been carried out. This job has provided the effective technology support in the research of the heritage computed tomography scan platform. This paper also has provided the information for teaching and learning activities of cultural studies.
18328733
Target Detecting Based on Improved WVD-HT for Radio Frequency Interference to UWB-SAR
Ultra-wideband synthetic aperture radar is interfered by ground clutter and artificial jamming. In many cases the targetpsilas scatter signal can be approximated as linear frequency modulated signals. Time-frequency Distribution, such as Winger-Ville distribution, provides a direct and effective analysis method of the time-frequency structures of linear frequency modulated signals. Meanwhile, the cross terms of the Winger-Ville distribution can be smoothed through the smoothed pseudo Winger-Ville distribution. Combine Hough transform the time-frequency concentration is improved and better precision of parameter estimation will be obtained. Target detecting and parameter estimation by the smoothed pseudo Winger-Ville distribution and Hough transform for frequency-modulated jamming are simulated in this paper. The method proved to be efficient for target detecting and parameter estimation of ultra-wideband synthetic aperture radar.