{"title":"结核菌通过区域生长的自动颜色分割:一种新方法","authors":"Chayadevi M L, Raju G T","doi":"10.1109/ICADIWT.2014.6814682","DOIUrl":null,"url":null,"abstract":"Medical image analysis is very challenging due to idiosyncrasies of medical profession. Object recognition with data mining techniques has helped doctors in case of medical emergencies for the image analysis, pattern identification and treatment. Over 180 million people died and more than one third of the population is carrier of Mycobacterium Tuberculosis (TB) bacteria as per the WHO statistics [1-5]. Segmentation of TB from the stained background is very challenging due to noise and debris in the image. In this paper, an automated segmentation of tuberculosis bacterium using image processing techniques is presented. Colour segmentation with region growing watershed algorithm is proposed for the bacterial identification.","PeriodicalId":339627,"journal":{"name":"The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Automated colour segmentation of Tuberculosis bacteria thru region growing: A novel approach\",\"authors\":\"Chayadevi M L, Raju G T\",\"doi\":\"10.1109/ICADIWT.2014.6814682\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Medical image analysis is very challenging due to idiosyncrasies of medical profession. Object recognition with data mining techniques has helped doctors in case of medical emergencies for the image analysis, pattern identification and treatment. Over 180 million people died and more than one third of the population is carrier of Mycobacterium Tuberculosis (TB) bacteria as per the WHO statistics [1-5]. Segmentation of TB from the stained background is very challenging due to noise and debris in the image. In this paper, an automated segmentation of tuberculosis bacterium using image processing techniques is presented. Colour segmentation with region growing watershed algorithm is proposed for the bacterial identification.\",\"PeriodicalId\":339627,\"journal\":{\"name\":\"The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICADIWT.2014.6814682\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICADIWT.2014.6814682","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated colour segmentation of Tuberculosis bacteria thru region growing: A novel approach
Medical image analysis is very challenging due to idiosyncrasies of medical profession. Object recognition with data mining techniques has helped doctors in case of medical emergencies for the image analysis, pattern identification and treatment. Over 180 million people died and more than one third of the population is carrier of Mycobacterium Tuberculosis (TB) bacteria as per the WHO statistics [1-5]. Segmentation of TB from the stained background is very challenging due to noise and debris in the image. In this paper, an automated segmentation of tuberculosis bacterium using image processing techniques is presented. Colour segmentation with region growing watershed algorithm is proposed for the bacterial identification.