The Experts below are selected from a list of 14723709 Experts worldwide ranked by ideXlab platform
P. S. Sathidevi - One of the best experts on this subject based on the ideXlab platform.
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A Fully Automated Gridding Technique for Real Composite cDNA Microarray Images
IEEE Access, 2020Co-Authors: Steffy Maria Joseph, P. S. SathideviAbstract:Genome-wide screening using microarrays of DNA will be of great use in the early diagnosis of diseases such as cancer and HIV. It also makes use of gene discovery, pharmacogenomics, toxicogenomics, and nutrigenomics for other applications. A DNA microarray image lays out an orderly arranged specific gene regions called spots. Microarray image analysis consists primarily of preprocessing, spot area Gridding, spot segmentation, and intensity extraction. The first two phases are focused on this work: preprocessing and Gridding. The experiment is conducted on real composite cDNA microarray images. A composite microarray image is formed by suitably stacking a red channel image and a green channel image acquired from a microarray experiment either in the RGB domain or in the GRB domain. The blue channel is kept as zero. In order to reduce the challenging problems of microarray images, an efficient preprocessing algorithm is proposed here for these composite images. We have developed a fully automated Gridding algorithm integrating global subgrid Gridding and local Gridding of spots. This technique extracts the structural information namely inter-subgrid spacing, inter-spot spacing and spot center position to achieve efficient Gridding. The traits of a microarray image are evaluated using three parameters namely Mean square error, Naturalness quality image evaluator and degree of contrast. The accuracy of the experimental results indicates that this combined preprocessing and Gridding technique performs better than existing competitive methods in SIB, GEO, SMD and DeRisi datasets which are most commonly used by the research community for microarray image analysis techniques.
Daniel Morris - One of the best experts on this subject based on the ideXlab platform.
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Blind Microarray Gridding: A New Framework
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2008Co-Authors: Daniel MorrisAbstract:In this paper, a completely blind microarray image Gridding framework is developed. The only input to the framework is the microarray image, which can be at any resolution, and the Gridding is accomplished with no prior assumptions. The framework includes an evolutionary algorithm (EA) and several novel methods for various stages of the Gridding process including subgrid detection. The approach toward Gridding differs significantly from most existing Gridding frameworks as it does not make use of 1D projections at any stage. Also proposed is the concept of regular spaced grid fitness. Rather than simply trying to identify the number of rows and columns within the grid, the approach includes a measure of fitness for possible grids. By attempting to minimize this fitness value, there is a proven measure of consistency to Gridding across multiple images. The framework is robust against high levels of image noise and a high percentage of nonexpressed/undetectable spots. The developed framework is thoroughly tested with a large number of simulated grids and several real microarray images.
Yudong Zhang - One of the best experts on this subject based on the ideXlab platform.
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Multilevel Segmentation Optimized by Physical Information for Gridding of Microarray Images
IEEE Access, 2019Co-Authors: Nianyin Zeng, Jianguo Chen, Min Du, Lyuchao Liao, Han Li, Yudong ZhangAbstract:As one of the great advances in modern technology, the microarray is widely used in many fields, including biomedical research, clinical diagnosis, and so on. Evidently, in order to extract the intensity of fluorescence bio-probes accurately, we need to pay special attention to the Gridding of microarray at first. To solve the poor effect of the traditional Otsu method for microarray Gridding, an innovative algorithm of Otsu optimized by multilevel thresholds is proposed to improve the accuracy and effectiveness of the microarray image Gridding and segmentation. The experimental results indicate that considering the physical information carried by microarrays, the improved algorithm of Otsu optimized by multilevel thresholds achieves high-quality Gridding and establishes the bio-spot coordinates more precisely. Compared with the traditional Otsu method, its Gridding error is reduced to zero, and the integrated relative error of bio-spot coordinates is decreased from 2.89% to 1.05%. This optimization of Otsu combined with physical information of spot-matrix will greatly improve the performance of segmentation so as to make the contribution to extracting the fluorescence intensity of microarray accurately.
Bojan Nikolic - One of the best experts on this subject based on the ideXlab platform.
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optimal Gridding and deGridding in radio interferometry imaging
Monthly Notices of the Royal Astronomical Society, 2019Co-Authors: Haoyang Ye, Stephen Gull, Bojan NikolicAbstract:In radio interferometry imaging, the Gridding procedure of convolving visibilities with a chosen Gridding function is necessary to transform visibility values into uniformly sampled grid points. We propose here a parameterised family of "least-misfit Gridding functions" which minimise an upper bound on the difference between the DFT and FFT dirty images for a given Gridding support width and image cropping ratio. When compared with the widely used spheroidal function with similar parameters, these provide more than 100 times better alias suppression and RMS misfit reduction over the usable dirty map. We discuss how appropriate parameter selection and tabulation of these functions allow for a balance between accuracy, computational cost and storage size. Although it is possible to reduce the errors introduced in the Gridding or deGridding process to the level of machine precision, accuracy comparable to that achieved by CASA requires only a lookup table with 300 entries and a support width of 3, allowing for a greatly reduced computation cost for a given performance.
Yan Li - One of the best experts on this subject based on the ideXlab platform.
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The Gridding Method for Image Reconstruction of Nonuniform Aperture Synthesis Radiometers
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Li Feng, Qingxia Li, Ke Chen, Yufang Li, Xiaolin Tong, Xinqiang Wang, Hailiang Lu, Yan LiAbstract:Nonuniform aperture synthesis radiometers (NASRs) are emerging in Earth remote sensing. The main disadvantage of NASRs is the complexity of the image reconstruction. In this letter, the Gridding method is introduced as an efficient method to reconstruct brightness temperature images for NASRs. In the Gridding method, a Voronoi diagram is applied to dividing the sampling region in the spatial frequency domain into a number of small cells, and the area of each cell is regarded as the density compensation factor. Meanwhile, a method is proposed to produce the outermost Voronoi cells for the outermost visibility samples. The errors induced by using the Gridding method are analyzed. The nonuniformity factor (NF) is defined to indicate the nonuniformity of a sampling pattern. The numerical results verify that the Gridding method can be applied to image reconstruction for NASRs and that the accuracy of reconstructed images is dependent on NF. The comparison between the Gridding method and the nonuniform fast Fourier transform algorithm shows that the Gridding method has lower computational complexity.
