{"title":"基于投影算法的微阵列图像自动识别","authors":"Y. Liu, Yong-de Zhang, Xianzheng Sha","doi":"10.1109/ICBBE.2010.5516457","DOIUrl":null,"url":null,"abstract":"To locate the spots in microarray images automatically, we combine the projection algorithm with statistic theory, making the gridding without absence and redundancy. The radius of spots could be estimated based on the improved algorithm, which is an important parameter in segmentation. Adaptive threshold is used to segment the image, and disk template generated automatically is used to detect the spots. The positive and weak spots could be detected correctly, so the methods are not only for the ideal images, but also for the images with many negative spots.","PeriodicalId":6396,"journal":{"name":"2010 4th International Conference on Bioinformatics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Automatic Recognition of Microarray Images Using Projection Algorithm\",\"authors\":\"Y. Liu, Yong-de Zhang, Xianzheng Sha\",\"doi\":\"10.1109/ICBBE.2010.5516457\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To locate the spots in microarray images automatically, we combine the projection algorithm with statistic theory, making the gridding without absence and redundancy. The radius of spots could be estimated based on the improved algorithm, which is an important parameter in segmentation. Adaptive threshold is used to segment the image, and disk template generated automatically is used to detect the spots. The positive and weak spots could be detected correctly, so the methods are not only for the ideal images, but also for the images with many negative spots.\",\"PeriodicalId\":6396,\"journal\":{\"name\":\"2010 4th International Conference on Bioinformatics and Biomedical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 4th International Conference on Bioinformatics and Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBBE.2010.5516457\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 4th International Conference on Bioinformatics and Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBBE.2010.5516457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Recognition of Microarray Images Using Projection Algorithm
To locate the spots in microarray images automatically, we combine the projection algorithm with statistic theory, making the gridding without absence and redundancy. The radius of spots could be estimated based on the improved algorithm, which is an important parameter in segmentation. Adaptive threshold is used to segment the image, and disk template generated automatically is used to detect the spots. The positive and weak spots could be detected correctly, so the methods are not only for the ideal images, but also for the images with many negative spots.