Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection
The advancement in biological and medical image acquisitions has allowed the development of numerous investigations in different fields supported by image analysis, from cell to physiological level. The complexity in the treatment of data, generated by image analysis, requires a structured methodolo...
- Autores:
-
Gamarra, Margarita
Zurek, Eduardo
Nieto Bernal, Wilson
Jimeno, Miguel
Sierra, Deibys
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7816
- Acceso en línea:
- https://hdl.handle.net/11323/7816
https://doi.org/10.1007/978-3-030-47679-3_3
https://repositorio.cuc.edu.co/
- Palabra clave:
- Spiral methodology
Bio-image informatics
Cell image processing
Respiratory Syncytial Virus
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection |
| title |
Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection |
| spellingShingle |
Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection Spiral methodology Bio-image informatics Cell image processing Respiratory Syncytial Virus |
| title_short |
Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection |
| title_full |
Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection |
| title_fullStr |
Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection |
| title_full_unstemmed |
Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection |
| title_sort |
Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection |
| dc.creator.fl_str_mv |
Gamarra, Margarita Zurek, Eduardo Nieto Bernal, Wilson Jimeno, Miguel Sierra, Deibys |
| dc.contributor.author.spa.fl_str_mv |
Gamarra, Margarita Zurek, Eduardo Nieto Bernal, Wilson Jimeno, Miguel Sierra, Deibys |
| dc.subject.spa.fl_str_mv |
Spiral methodology Bio-image informatics Cell image processing Respiratory Syncytial Virus |
| topic |
Spiral methodology Bio-image informatics Cell image processing Respiratory Syncytial Virus |
| description |
The advancement in biological and medical image acquisitions has allowed the development of numerous investigations in different fields supported by image analysis, from cell to physiological level. The complexity in the treatment of data, generated by image analysis, requires a structured methodology for software development. In this paper we proposed a framework to develop a software solution with a Service-Oriented Architecture (SOA) applied to the analysis of biological images. The framework is completed with a novel image analysis methodology that would help researchers to achieve better results in their image analysis projects. We evaluate our proposal in a scientific project related to cell image analysis. |
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2020 |
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2020 |
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2021-02-02T22:13:24Z |
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2021-02-02T22:13:24Z |
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https://doi.org/10.1007/978-3-030-47679-3_3 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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eng |
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1. Peng, H.: Bioimage informatics: a new area of engineering biology. Bioinformatics 24, 1827–1836 (2008). 3. Gamarra, M., Zurek, E., Nieto, W., Jimeno, M., Sierra, D.: A service-oriented architecture for bioinformatics: an application in cell image analysis. In: Rocha, Á., Correia, A.M., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST 2017. AISC, vol. 569, pp. 724–734. Springer, Cham (2017). 4. Zorrilla, M., García-Saiz, D.: A service oriented architecture to provide data mining services for non-expert data miners. Decis. Support Syst. 55, 399–411 (2013). 5. Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012). 6. Abramoff, M.D., Magalhães, P.J., Ram, S.J.: Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004). 7. Yoo, T.S., Ackerman, M.J., Lorensen, W.E., Schroeder, W., Chalana, V., Aylward, S., et al.: Engineering and algorithm design for an image processing API: a technical report on ITK–the Insight Toolkit. Stud. Health Technol. Inform. 85, 586–592 (2002) 8. Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., et al.: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006). 9. Pelet, S., Dechant, R., Lee, S.S., van Drogen, F., Peter, M.: An integrated image analysis platform to quantify signal transduction in single cells. Integr. Biol. (Camb). 4, 1274–1282 (2012). 10. Kvilekval, K., Fedorov, D., Obara, B., Singh, A., Manjunath, B.S.: Bisque: a platform for bioimage analysis and management. Bioinformatics 26, 544–552 (2010). 11. Ahmed, Z., Zeeshan, S., Dandekar, T.: Developing sustainable software solutions for bioinformatics by the “Butterfly” paradigm. F1000Research 3, 71 (2014). 12. Sharma, A., Vidyapeeth, J.R.N.R.: Application of AOP methodology in eclipse-AJDT environment for developing bioinformatics software (n.d.). 13. Al-Otaibi, N.M., Noaman, A.Y.: Biological data integration using SOA. Int. J. Comput. Electr. Autom. Control Inf. Eng. 5, 74–79 (2011) 14. Castillo, J.C., Almeida, F., Blanco, V., Ramírez, M.C.: Web services based platform for the cell counting problem. In: Lopes, L., et al. (eds.) Euro-Par 2014. LNCS, vol. 8805, pp. 83–92. Springer, Cham (2014). 15. Tosi, S., Bardia, L., Filgueira, M., Calon, A., Colombelli, J.: LOBSTER: an environment to design bioimage analysis workflows for large and complex fluorescence microscopy data. Bioimage Inform. 36(8), 2634–2635 (2019). 16. Boehm, B.W.: A spiral model of software development and enhancement. Comput. (Long. Beach. Calif.). 21, 61–72 (1988). 17. Moeslund, T.B.: Image Acquisition, pp. 7–24 (2012). 18. Gonzalez, R.C.: Digital Image Processing. Pearson Education, Upper Saddle River (2009) 19. Oliveira, R.B., Papa, J.P., Pereira, A.S., Tavares, J.M.R.S.: Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput. Appl., 1–24 (2016). 20. CMMI® for Development, Version 1.3—CMMI Institute. CMMI Institute (2010) 21. González-Castaño, D.M., Pena, J., Gómez, F., Gago-Arias, A., González-Castaño, F.J., Rodríguez-Silva, D.A., et al.: eIMRT: a web platform for the verification and optimization of radiation treatment plans. J. Appl. Clin. Med. Phys. 10, 2998 (2009) 22. Xiang, X.: Service-oriented architecture for integration of bioinformatic data and applications. University of Notre Dame (2007) 23. Gamarra, M., Zurek, E., Escalante, H.J., Hurtado, L., San-Juan-Vergara, H.: Split and merge watershed: a two-step method for cell segmentation in fluorescence microscopy images. Biomed. Signal Process Control (53) (2019). |
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Gamarra, Margarita7754112a969e290336bee9412aa2822eZurek, Eduardoee2a3409eb16ffc034b7c98de6e895a7Nieto Bernal, Wilsond8663a500413b6fe34477d35949fd228Jimeno, Miguel450cf88630e82f8ce83cd95bad30c6c4Sierra, Deibys41b7be45eb8d10d439e8ec17fa7ceebc2021-02-02T22:13:24Z2021-02-02T22:13:24Z2020https://hdl.handle.net/11323/7816https://doi.org/10.1007/978-3-030-47679-3_3Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The advancement in biological and medical image acquisitions has allowed the development of numerous investigations in different fields supported by image analysis, from cell to physiological level. The complexity in the treatment of data, generated by image analysis, requires a structured methodology for software development. In this paper we proposed a framework to develop a software solution with a Service-Oriented Architecture (SOA) applied to the analysis of biological images. The framework is completed with a novel image analysis methodology that would help researchers to achieve better results in their image analysis projects. We evaluate our proposal in a scientific project related to cell image analysis.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Computer Sciencehttps://link.springer.com/chapter/10.1007/978-3-030-47679-3_3Spiral methodologyBio-image informaticsCell image processingRespiratory Syncytial VirusSpiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infectionArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Peng, H.: Bioimage informatics: a new area of engineering biology. Bioinformatics 24, 1827–1836 (2008).3. Gamarra, M., Zurek, E., Nieto, W., Jimeno, M., Sierra, D.: A service-oriented architecture for bioinformatics: an application in cell image analysis. In: Rocha, Á., Correia, A.M., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST 2017. AISC, vol. 569, pp. 724–734. Springer, Cham (2017).4. Zorrilla, M., García-Saiz, D.: A service oriented architecture to provide data mining services for non-expert data miners. Decis. Support Syst. 55, 399–411 (2013).5. Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).6. Abramoff, M.D., Magalhães, P.J., Ram, S.J.: Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).7. Yoo, T.S., Ackerman, M.J., Lorensen, W.E., Schroeder, W., Chalana, V., Aylward, S., et al.: Engineering and algorithm design for an image processing API: a technical report on ITK–the Insight Toolkit. Stud. Health Technol. Inform. 85, 586–592 (2002)8. Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., et al.: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).9. Pelet, S., Dechant, R., Lee, S.S., van Drogen, F., Peter, M.: An integrated image analysis platform to quantify signal transduction in single cells. Integr. Biol. (Camb). 4, 1274–1282 (2012).10. Kvilekval, K., Fedorov, D., Obara, B., Singh, A., Manjunath, B.S.: Bisque: a platform for bioimage analysis and management. Bioinformatics 26, 544–552 (2010).11. Ahmed, Z., Zeeshan, S., Dandekar, T.: Developing sustainable software solutions for bioinformatics by the “Butterfly” paradigm. F1000Research 3, 71 (2014).12. Sharma, A., Vidyapeeth, J.R.N.R.: Application of AOP methodology in eclipse-AJDT environment for developing bioinformatics software (n.d.).13. Al-Otaibi, N.M., Noaman, A.Y.: Biological data integration using SOA. Int. J. Comput. Electr. Autom. Control Inf. Eng. 5, 74–79 (2011)14. Castillo, J.C., Almeida, F., Blanco, V., Ramírez, M.C.: Web services based platform for the cell counting problem. In: Lopes, L., et al. (eds.) Euro-Par 2014. LNCS, vol. 8805, pp. 83–92. Springer, Cham (2014).15. Tosi, S., Bardia, L., Filgueira, M., Calon, A., Colombelli, J.: LOBSTER: an environment to design bioimage analysis workflows for large and complex fluorescence microscopy data. Bioimage Inform. 36(8), 2634–2635 (2019).16. Boehm, B.W.: A spiral model of software development and enhancement. Comput. (Long. Beach. Calif.). 21, 61–72 (1988).17. Moeslund, T.B.: Image Acquisition, pp. 7–24 (2012).18. Gonzalez, R.C.: Digital Image Processing. Pearson Education, Upper Saddle River (2009)19. Oliveira, R.B., Papa, J.P., Pereira, A.S., Tavares, J.M.R.S.: Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput. Appl., 1–24 (2016).20. CMMI® for Development, Version 1.3—CMMI Institute. CMMI Institute (2010)21. González-Castaño, D.M., Pena, J., Gómez, F., Gago-Arias, A., González-Castaño, F.J., Rodríguez-Silva, D.A., et al.: eIMRT: a web platform for the verification and optimization of radiation treatment plans. J. Appl. Clin. Med. Phys. 10, 2998 (2009)22. Xiang, X.: Service-oriented architecture for integration of bioinformatic data and applications. University of Notre Dame (2007)23. Gamarra, M., Zurek, E., Escalante, H.J., Hurtado, L., San-Juan-Vergara, H.: Split and merge watershed: a two-step method for cell segmentation in fluorescence microscopy images. Biomed. 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