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Relevant bibliographies by topics / Microarray image analysis

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Academic literature on the topic 'Microarray image analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Microarray image analysis"

1

Wang, Zhiyou, Xiaoqing Huang, and Zhiqiang Cheng. "Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis." Biosensors 8, no. 3 (2018): 85. http://dx.doi.org/10.3390/bios8030085.

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An automatic spot identification method is developed for high throughput surface plasmon resonance imaging (SPRi) analysis. As a combination of video accessing, image enhancement, image processing and parallel processing techniques, the method can identify the spots in SPRi images of the microarray from SPRi video data. In demonstrations of the method, SPRi video data of different protein microarrays were processed by the method. Results show that our method can locate spots in the microarray accurately regardless of the microarray pattern, spot-background contrast, light nonuniformity and spo

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Roszkowiak, Lukasz, and Carlos Lopez. "PATMA: parser of archival tissue microarray." PeerJ 4 (December 1, 2016): e2741. http://dx.doi.org/10.7717/peerj.2741.

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The tissue microarrays are commonly used in modern pathology for cancer tissue evaluation, as it is a very potent technique. Tissue microarray slides are often scanned to perform computer-aided histopathological analysis of the tissue cores. For processing the image, splitting the whole virtual slide into images of individual cores is required. The only way to distinguish cores corresponding to specimens in the tissue microarray is through their arrangement. Unfortunately, distinguishing the correct order of cores is not a trivial task as they are not labelled directly on the slide. The main a

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Br�ndle, Norbert, Horst Bischof, and Hilmar Lapp. "Robust DNA microarray image analysis." Machine Vision and Applications 15, no. 1 (2003): 11–28. http://dx.doi.org/10.1007/s00138-002-0114-x.

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Sivalakshmi, Bolem, and N. Naga Malleswara Rao. "Microarray Image Analysis Using Genetic Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (2016): 561. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp561-567.

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<p>Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. This paper presents microarray image analysis using Genetic Algorithm. A new algorithm for microarray image contrast enhancement is presented using Genetic Algorithm. Contrast enhancement is crucial step in e

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Marzolf, Bruz, and Michael H. Johnson. "Validation of microarray image analysis accuracy." BioTechniques 36, no. 2 (2004): 304–8. http://dx.doi.org/10.2144/04362mt01.

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Rahnenführer, J. "Image Analysis for cDNA Microarrays." Methods of Information in Medicine 44, no. 03 (2005): 405–7. http://dx.doi.org/10.1055/s-0038-1633984.

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Summary Objectives: We characterize typical problems encountered in microarray image analysis and present algorithmic approaches dealing with background estimation, spot identification and intensity extraction. Validation of the quality of resulting measurements is discussed. Methods: We describe sources for errors in microarray images and present algorithms that have been specifically developed to deal with such experimental imperfections. Results: For the image analysis of hybridization experiments, discriminating spot regions from a background is the most critical step. Spot shape detection

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Fouad, Islam A., Mai S. Mabrouk, and Amr A. Sharawy. "A Fully Automated Method for Noisy cDNA Microarray Image Quantification." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (2013): 2330–40. http://dx.doi.org/10.24297/ijct.v11i3.1170.

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DNA microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. Image processing is an important aspect of microarray experiments, the primary purpose of the image analysis step is to extract numerical foreground and background intensities for the red and green channels for each spot on the microarray. The background intensities are used to correct the foreground intensities for local variation on the array surface, resulting in corrected red and green intensities for each spot that can be considered as

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Kakumani, Arunakumari, Kaustubha A. Mendhurwar, and Rajasekhar Kakumani. "Microarray Image Denoising using Independent Component Analysis." International Journal of Computer Applications 1, no. 11 (2010): 84–90. http://dx.doi.org/10.5120/234-388.

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White, A. M., D. S. Daly, A. R. Willse, M. Protic, and D. P. Chandler. "Automated Microarray Image Analysis Toolbox for MATLAB." Bioinformatics 21, no. 17 (2005): 3578–79. http://dx.doi.org/10.1093/bioinformatics/bti576.

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Katzer, Mathias, Franz Kummert, and Gerhard Sagerer. "S02.4: Methods for automatic microarray image analysis." Biometrical Journal 46, S1 (2004): 5. http://dx.doi.org/10.1002/bimj.200490100.

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Dissertations / Theses on the topic "Microarray image analysis"

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Stephens, Nathan W. "A comparison of genetic microarray analyses : a mixed models approach versus the significance analysis of microarrays /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1604.pdf.

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Fraser, Karl. "cDNA microarray image analysis : a fully automated framework." Thesis, Brunel University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429240.

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Amaral, Telmo. "Analysis of breast tissue microarray spots." Thesis, University of Dundee, 2010. https://discovery.dundee.ac.uk/en/studentTheses/0a83915d-2f11-4b89-9c24-8dc3c15346f2.

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Tissue microarrays (TMAs) are a high-throughput technique that facilitates the survey of very large numbers of tumours, important both in clinical and research applications. However, the assessment of stained TMA sections is laborious and still needs to be carried manually, constituting a bottleneck in the pathologist?s work-flow. This process is also prone to perceptual errors and observer variability.Thus, there is strong motivation for the development of automated quantitative analysis of TMA image data. The analysis of breast TMA sections subjected to nuclear immunostaining begins with the

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Erkers, Julia. "Towards automatic smartphone analysis for point-of-care microarray assays." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-280663.

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Poverty and long distances are two reasons why some people in the third world countries hasdifficulties seeking medical help. A solution to the long distances could be if the medical carewas more mobile and diagnostically tests could be performed on site in villages. A new pointof-care test based on a small blood shows promising results both in run time and mobility.However, the method still needs more advanced equipment for analysis of the resultingmicroarray. This study has investigated the potential to perform the analysis within asmartphone application, performing all steps from image capt

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Reynolds, Derrick James. "Genetic Dissection of Triterpenoid Saponin Production in Chenopodium quinoa Using Microarray Analysis." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3309.pdf.

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Strömberg, Sara. "Antibody-based Profiling of Expression Patterns using Cell and Tissue Microarrays." Doctoral thesis, Uppsala University, Department of Genetics and Pathology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8680.

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<p>In this thesis, methods to study gene and protein expression in cells and tissues were developed and utilized in combination with protein-specific antibodies, with the overall objective to attain greater understanding of protein function.</p><p>To analyze protein expression in <i>in vitro</i> cultured cell lines, a cell microarray (CMA) was developed, facilitating antibody-based protein profiling of cell lines using immunohistochemistry (IHC). Staining patterns in cell lines were analyzed using image analysis, developed to automatically identify cells and immunohistochemical staining, provi

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Vencio, Ricardo Zorzetto Nicoliello. "Análise estatística na interpretação de imagens: microarranjos de DNA e ressonância magnética funcional." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-16032007-164424/.

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O objetivo deste trabalho é apresentar os métodos originais em Bioinformática desenvolvidos para a análise estatística na interpretação dos dados de duas técnicas baseadas em imagens: a técnica de microarranjos de DNA e a técnica de ressonância magnética funcional. O interesse principal é abordar essas técnicas experimentais quando enfrenta-se uma situação clara de amostras escassas, isto é, quando existem relativamente poucas observações experimentais do fenômeno estudado, sendo a análise individual/personalizada o representante extremo desta situação, que tem que ser resolvida. Para tanto, o

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O'Neill, Paul. "Improved analysis of microarray images." Thesis, Brunel University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435755.

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Bergemann, Tracy L. "Image analysis and signal extraction from cDNA microarrays /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/9603.

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Pendleton, Carly R. "A simulation-based approach for evaluating gene expression analyses /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1753.pdf.

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Books on the topic "Microarray image analysis"

1

Zidong, Wang, and Liu Xiaohui, eds. Microarray image analysis: An algorithmic approach. Chapman & Hall/CRC, 2010.

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Fraser, Karl. Microarray image analysis: An algorithmic approach. Chapman & Hall/CRC, 2010.

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Rueda, Luis, ed. Microarray Image and Data Analysis. CRC Press, 2018. http://dx.doi.org/10.1201/9781315215785.

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Microarray Image and Data Analysis: Theory and Practice. Taylor & Francis Group, 2014.

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L, Kamberova Gerda, and Shah Shishir Kirit 1971-, eds. DNA array image analysis: Nuts & bolts. DNA Press, 2002.

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Kamberova, Gerda, and Shishir Shah. DNA Array Image Analysis: Nuts & Bolts (Nuts & Bolts series). DNA Press, 2002.

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Book chapters on the topic "Microarray image analysis"

1

Yu, Sean, Pranav Joshi, Dong Woo Lee, and Moo-Yeal Lee. "High-Content Image Analysis." In Microarray Bioprinting Technology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46805-1_7.

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Saucan, Emil, and Eli Appleboim. "Curvature Based Clustering for DNA Microarray Data Analysis." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_50.

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Mastandrea, Fernando, and Álvaro Pardo. "Processing of Microarray Images." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_112.

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O’Neill, Paul, George D. Magoulas, and Xiaohui Liu. "Obtaining Quality Microarray Data via Image Reconstruction." In Advances in Intelligent Data Analysis V. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45231-7_34.

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Ruosaari, Salla, and Jaakko Hollmén. "Image Analysis for Detecting Faulty Spots from Microarray Images." In Discovery Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36182-0_23.

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García, V., J. S. Sánchez, L. Cleofas-Sánchez, H. J. Ochoa-Domínguez, and F. López-Orozco. "An Insight on the ‘Large G, Small n’ Problem in Gene-Expression Microarray Classification." In Pattern Recognition and Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_53.

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Kiranmai, D., G. Lavanya Devi, and M. Murali Krishna. "Designing of Algorithm for Image Analysis in Genotyping Microarray Data Analysis." In Cognitive Science and Health Bioinformatics. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6653-5_1.

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Akila, S., and S. Allin Christe. "An Experimental Analysis of Gene Feature Selection and Classification Methods for Cancer Microarray." In Applied Computer Vision and Image Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4029-5_20.

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Ryan, Denise, Laoighse Mulrane, Elton Rexhepaj, and William M. Gallagher. "Tissue Microarrays and Digital Image Analysis." In Methods in Molecular Biology. Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-849-2_6.

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Jörnsten, Rebecka, Yehuda Vardi, and Cun-Hui Zhang. "On the Bitplane Compression of Microarray Images." In Statistical Data Analysis Based on the L1-Norm and Related Methods. Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8201-9_34.

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Conference papers on the topic "Microarray image analysis"

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LIATSIS, P., and M. A. NAZARBOLAND. "MICROARRAY IMAGE ANALYSIS." In Proceedings of the 9th International Workshop on Systems, Signals and Image Processing. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776266_0078.

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Tozduman, Ersin, and Songul Albayrak. "cDNA microarray image analysis." In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130308.

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Muresan, L., B. Heise, E. P. Klement, and J. Kybic. "Quantitative analysis of microarray images." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530295.

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Qi, Fei, and Chengying Hua. "Efficient automated microarray image analysis." In Second International Conference on Image and Graphics, edited by Wei Sui. SPIE, 2002. http://dx.doi.org/10.1117/12.477198.

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Tiwari, Richa. "Comparison of microarray image analysis software." In the 46th Annual Southeast Regional Conference. ACM Press, 2008. http://dx.doi.org/10.1145/1593105.1593125.

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Wu, Shuanhu, Chuangcun Wang, and Limin Sun. "A novel segmentation and compression scheme for cDNA microarray images." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.654799.

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Zacharia, Eleni, and Dimitirs Maroulis. "Microarray image analysis based on an evolutionary approach." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761749.

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Chen, Wei-Bang, Chengcui Zhang, Wen-Lin Liu, and Richa Tiwari. "MIA: A UIMA-Based Microarray Image Analysis System." In Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007). IEEE, 2007. http://dx.doi.org/10.1109/ism.workshops.2007.24.

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Infantino, Ignazio, Carmelo Lodato, and Salvatore Lopes. "Testing and Evaluation of Microarray Image Analysis Software." In 2008 International Conference on Complex, Intelligent and Software Intensive Systems. IEEE, 2008. http://dx.doi.org/10.1109/cisis.2008.19.

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Chen, Wei-Bang, Chengcui Zhang, Wen-Lin Liu, and Richa Tiwari. "MIA: A UIMA-Based Microarray Image Analysis System." In Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007). IEEE, 2007. http://dx.doi.org/10.1109/ismw.2007.4475953.

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