期刊论文详细信息
GigaScience
Using image mapping towards biomedical and biological data sharing
Dayang Nurfatimah Awang Iskandar1  Nurzi Juana Mohd Zaizi2 
[1] Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, 94300, Malaysia;Department of Computer Science, Heriot-Watt University, Edinburgh, Scotland, EH14 4AS, UK
关键词: Image mapping;    Biomedical image;    Biomedical data;    Spatial relations;    Data integration;   
Others  :  861525
DOI  :  10.1186/2047-217X-2-12
 received in 2013-05-10, accepted in 2013-09-12,  发布年份 2013
PDF
【 摘 要 】

Image-based data integration in eHealth and life sciences is typically concerned with the method used for anatomical space mapping, needed to retrieve, compare and analyse large volumes of biomedical data. In mapping one image onto another image, a mechanism is used to match and find the corresponding spatial regions which have the same meaning between the source and the matching image. Image-based data integration is useful for integrating data of various information structures. Here we discuss a broad range of issues related to data integration of various information structures, review exemplary work on image representation and mapping, and discuss the challenges that these techniques may bring.

【 授权许可】

   
2013 Zaizi and Iskandar; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20140725002041769.pdf 544KB PDF download
43KB Image download
【 图 表 】

【 参考文献 】
  • [1]Haux R, Ammenwerth E, Herzog W, Knaup P: Health care in the information society. A prognosis for the year 2013. Int J Med Inform 2002, 66:3-21.
  • [2]Kulikowski CA, Gong L, Mezrich RS: Knowledge-based medical image analysis and representation for integrating content definition with the radiological report. Methods Inf Med 1995, 34:96-103.
  • [3]Rosse C, Mejino JLV: The foundational model of anatomy ontology. In Anatomy Ontologies for Bioinformatics: Principles and Practise. Edited by Burger A, Davidson D, Baldock R. London: Springer-Verlag; 2008:59-117.
  • [4]Bittner T: Logical properties of foundational mereogeometrical relations in bio-ontologies. Appl Ontology 2009, 4(2):109-138.
  • [5]Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, Goldberg L, Eilbeck K, Ireland A, Mungall C, Consortium O, Leontis N, Rocca-Serra P, Ruttenberg A, Sansone S, Scheuermann R, Shah N, Whetzel P, Lewis S: The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol 2007, 25(11):1251-1255.
  • [6]Zaizi NJM, Burger A: Towards spatial description-based integration of biomedical atlases. In 4th ICST International Conference on eHealth (eHealth 2011): 21-23 November; Malaga, Spain. Edited by Kostkova P, Szomszor M, Fowler D. Berlin, Heidelberg: Springer-Verlag; 2012:196-203.
  • [7]Alex AB, Ricky KT: Medical Imaging Informatics. New York: Springer; 2010.
  • [8]Iskandar D: Visual ontology query language. 1st International Conference on Networked Digital Technologies (NDT ‘09). 2009, 65-70.
  • [9]Boccignone G, Napoletano P, Ferraro M: Embedding diffusion in variational bayes: A technique for segmenting images. Int J Pattern Recognit Artif Intell World Sci 2008, 22:811-827.
  • [10]Wyawahare MV, Patil PM, Abhyankar HK: Image registration techniques: an overview. J Image Process Pattern Recognit 2009, 2(3):11-28.
  • [11]Izard C, Jedynak B: Bayesian registration for anatomical landmark detection. Proceedings of 3rd IEEE International Symposium on Biomedical Imaging. 2006, 856-859.
  • [12]Khaissidi G, Tairi H, Aarab A: A fast medical image registration using feature points. ICGST-GVIP J 2009, 9(3):19-24.
  • [13]Guest E, Berry E, Baldock RA, Fidrich M, Smith MA: Robust point corespondence applied to two and three dimensional image registration. IEEE Trans Pattern Anal Mach Intell 2001, 23(2):1-15.
  • [14]Bittner T, Donelly M, Goldberg LJ, Neuhaus F: Modeling principles and methodologies - spatial representation and reasoning. In Anatomy Ontologies for Bioinformatics: Principles and Practise. Edited by Burger A, Davidson D, Baldock R. London: Springer-Verlag; 2008:307-326.
  • [15]Li S: Combining topological and directional information for spatial reasoning. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI‘07. San Francisco: Morgan Kaufmann Publishers Inc.; 2007:435-440.
  • [16]Schwering A: Evaluation of a semantic similarity measure for natural language spatial relations. In Proceedings of the 8th International Conference on Spatial Information Theory, COSIT‘07. Berlin, Heidelberg: Springer-Verlag; 2007:116-132.
  • [17]Egenhofer MJ, Herring J: Categorizing binary topological relations between regions, lines and points in geographic databases. In Tech. Report.. Department of Surveying Engineering, University of Maine; 1991.
  • [18]Abella A, Kender JR: From images to sentences via spatial relations. Proceedings of the Integration of Speech and Image Understanding. 1999, 117-146.
  • [19]Liu Y, Guo Q, Kelly M: A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis. ISPRS J Photogrammetry Remote Sensing 2008, 63(4):461-475.
  • [20]Chen J, Jia H, Liu D, Zhang C: Composing cardinal direction relations basing on interval algebra. In Proceedings of the 4th International Conference on Knowledge Science, Engineering and Management, KSEM‘10. Berlin, Heidelberg: Springer-Verlag; 2010:114-124.
  • [21]Frank AU: Qualitative spatial reasoning: cardinal directions as an example. Int J Geogr Inf Sci 1996, 10(3):269-290.
  • [22]Freksa C: Using orientation information for qualitative spatial reasoning. In Proceedings of the International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio-Temporal Reasoning in Geographic Space. London: Springer-Verlag; 1992:162-178.
  • [23]Ligozat G: Reasoning about cardinal directions. J Vis Lang Comput 1998, 9:23-44.
  • [24]Papadias D, Sellis T: Qualitative representation of spatial knowledge in two-dimensional space. VLDB J 1994, 3(4):479-516.
  • [25]Mechouche A, Morandi X, Golbreich C, Gibaud B: A hybrid system for the semantic annotation of Sulco-Gyral anatomy in MRI images. In Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention - Part I, MICCAI ‘08. Berlin, Heidelberg: Springer-Verlag; 2008:807-814.
  • [26]Hudelot C, Atif J, Bloch I: Fuzzy spatial relation ontology for image interpretation. Fuzzy Sets Syst 2008, 159(15):1929-1951.
  • [27]Du S, Qin Q, Chen D, Wang L: Spatial data query based on natural language spatial relations. Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS ‘05), 2005, 1210-1213.
  • [28]Chang CC, Wu TC: An exact match retrieval scheme based upon principal component analysis. Pattern Recogn Lett 1995, 16(5):465-470.
  • [29]Guru DS, Punitha P: An invariant scheme for exact match retrieval of symbolic images based upon principal component analysis. Pattern Recogn Lett 2004, 25:73-86.
  • [30]Karouia I, Zagrouba E: New image matching method based on spatial region interrelationships. Proceedings of the 4th International Conference on Innovations in Information Technology (IIT ‘07). 2007, 675-679.
  • [31]Zhou XM, Ang CH, Ling TW: Image retrieval based on object’s orientation spatial relationship. Pattern Recogn Lett 2001, 22(5):469-477.
  • [32]Kulkarni MA, Joshi RC: Content-based image retrieval by spatial similarity. Def Sci J 2002, 52(3):285-291.
  • [33]Majumdar AK, Bhattacharya I, Saha AK: An object-oriented fuzzy data model for similarity detection in image databases. IEEE Trans Knowl Data Eng 2002, 14(5):1186-1189.
  • [34]Wang YH: Image indexing and similarity retrieval based on a new spatial relation model. 2001 International Conference on Distributed Computing Systems Workshops (ICDCSW ‘01). 2001, 396-401.
  • [35]Yang L, Zhongjian T: A novel approach for image representation and matching based on mixed graph structure. Computational Intelligence and Software Engineering (CiSE 2009). 2009, 1-4.
  • [36]Izard C, Jedynak B, Stark C: Spline-based probabilistic model for anatomical landmark detection. In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2006),. Edited by Larsen R, Nielsen M, Sporring J. Berlin, Heidelberg: Springer-Verlag; 2006:849-856.
  • [37]Georgescu B, Zhou XS, Comaniciu D, Gupta A: Database-guided segmentation of anatomical structures with complex appearance. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR‘05). Washington: IEEE Computer Society; 2005:429-436.
  • [38]Potesil V, Kadir T, Platsch G, Brady M: Improved anatomical landmark localization in medical images using dense matching of graphical models. In Proceedings of the British Machine Vision Conference.. BMVA Press; 2010:37.1-37.10.
  • [39]Seifert S, Barbu A, Zhou SKevin, Liu D, Feulner J, Huber M, Suehling M, Cavallaro A, Comaniciu D: Hierarchical parsing and semantic navigation of full body CT data. Proc. SPIE 7259, Medical Imaging 2009: Image Processing. 2009, 725902-725902–8.
  • [40]Allen Brain Atlas http://developingmouse.brain-map.org webcite
  • [41]Christiansen JH, Yang Y, Venkataraman S, Richardson L, Stevenson P, Burton N, Baldock RA, Davidson DR: EMAGE: a spatial database of gene expression patterns during mouse embryo development. Nucleic Acids Res 2010, 34(suppl 1):D637—D641.
  • [42]Baldock RA, Bard JB, Burger A, Burton N, Christiansen J, Feng G, Hill B, Houghton D, Kaufman M, Rao J, Sharpe J, Ross A, Stevenson P, Venkataraman S, Waterhouse A, Yang Y, Davidson DR: EMAP and EMAGE - a framework for understanding spatially organized data. Neuroinformatics 2003, 4:309-325.
  • [43]Gensat Brain Atlas of Gene Expression http://www.gensat.org/index.html webcite
  • [44]McLeod K, Burger A: Towards the use of argumentation in bioinformatics: a gene expression case study. Bioinformatics 2008, 24:304-312.
  • [45]Boline J, Lee EF, Toga AW: Digital atlases as a framework for data sharing. Front Neurosci 2008, 2:100-106.
  • [46]Yang C, Zeng E, Li T, Narasimhan G: Clustering genes using gene expression and text literature data. In Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference. Washington: IEEE Computer Society; 2005:329-340.
  • [47]Hearst MA: Untangling text data mining. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, ACL ‘99. Stroudsburg: Association for Computational Linguistics; 1999:3-10.
  • [48]Pasquier N, Pasquier C, Brisson L, Collard M: Mining gene expression data using domain knowledge. Int J Softw Inform 2008, 2(2):215-231.
  • [49]Hemert J, Baldock R: Mining spatial gene expression data for association rules. In Bioinformatics Research and Development,. Edited by Hochreiter S, Wagner R. Berlin, Heidelberg: Springer; 2007:66-76.
  • [50]Schaefer G, Nakashima T: Data mining of gene expression data by fuzzy and hybrid fuzzy methods. IEEE Inf Technol Biomed 2010, 14:23-29.
  • [51]Gerner M, Nenadic G, Bergman CM: An exploration of mining gene expression mentions and their anatomical locations from biomedical text. In Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, BioNLP ‘10. Stroudsburg: Association for Computational Linguistics; 2010:72-80.
  文献评价指标  
  下载次数:14次 浏览次数:30次