{"title":"cDNA microarray image analysis","authors":"Ersin Tozduman, S. Albayrak","doi":"10.1109/BIYOMUT.2009.5130308","DOIUrl":null,"url":null,"abstract":"cDNA microarray image analysis has grown very important these days because of the growing field which it's part of: microarray expression analysis. In this work we propose a new cDNA microarray image analysis system. A cDNA microarray analysis system consists of three main parts which are Gridding, Segmentation and Information Extraction, respectively. In gridding stage we propose a new automated gridding technique which is based on mathematical morphology. For segmentation we propose two different algorithm which are based on K-Means clustering and Fuzzy C-Means clustering respectively. On the final stage, information extraction, our proposed system uses a classical approach which calculates background values locally. The proposed system has open parts in the way of developement and is still being worked on.","PeriodicalId":119026,"journal":{"name":"2009 14th National Biomedical Engineering Meeting","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 14th National Biomedical Engineering Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIYOMUT.2009.5130308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
cDNA microarray image analysis has grown very important these days because of the growing field which it's part of: microarray expression analysis. In this work we propose a new cDNA microarray image analysis system. A cDNA microarray analysis system consists of three main parts which are Gridding, Segmentation and Information Extraction, respectively. In gridding stage we propose a new automated gridding technique which is based on mathematical morphology. For segmentation we propose two different algorithm which are based on K-Means clustering and Fuzzy C-Means clustering respectively. On the final stage, information extraction, our proposed system uses a classical approach which calculates background values locally. The proposed system has open parts in the way of developement and is still being worked on.