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		<doi>10.1109/SIBGRAPI.2008.14</doi>
		<citationkey>NomaPardCesa:2008:StMa2D</citationkey>
		<title>Structural Matching of 2D Electrophoresis Gels using Graph Models</title>
		<format>Printed, On-line.</format>
		<year>2008</year>
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		<author>Noma, Alexandre,</author>
		<author>Pardo, Alvaro,</author>
		<author>Cesar-Jr, Roberto M.,</author>
		<affiliation>IME-USP, Department of Computer Science, University of Sao Paulo, Brazil</affiliation>
		<affiliation>DIE, Faculty of Engineering and Technologies, Catholic University of Uruguay</affiliation>
		<affiliation>IME-USP, Department of Computer Science, University of Sao Paulo, Brazil</affiliation>
		<editor>Jung, Cláudio Rosito,</editor>
		<editor>Walter, Marcelo,</editor>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 21 (SIBGRAPI)</conferencename>
		<conferencelocation>Campo Grande, MS, Brazil</conferencelocation>
		<date>12-15 Oct. 2008</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>2D electrophoresis gels, graph matching, structural pattern recognition, deformation graph, graph models, structural matching.</keywords>
		<abstract>2D electrophoresis is a well known method for protein separation which is extremely useful in the field of proteomics. Each spot in the image represents a protein accumulation and the goal is to perform a differential analysis between pairs of images to study changes in protein content. It is thus necessary to register two images by finding spot correspondences. Although it may seem a simple task, generally, the manual processing of this kind of images is very cumbersome. The complete task of individual spot matching and gel registration is a complex and time consuming process when strong variations between corresponding sets of spots are expected. Besides, because an one-to-one mapping is expected between the two images, missing spots there may exist on both images (i.e. spots without correspondence). In order to solve this problem, this paper proposes a new distance together with a correspondence estimation algorithm based on graph matching which takes into account the structural information between the detected spots. Each image is represented by a graph and the task is to find an isomorphism between subgraphs. Successful experimental results using real data are presented, including a comparative performance evaluation. .</abstract>
		<language>en</language>
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