Eisaku Ohta, Y. Mitsukura, M. Fukumi, N. Akamatsu, M. Yasutomo
{"title":"An extraction method of liver tumors by using genetic algorithms and neural networks","authors":"Eisaku Ohta, Y. Mitsukura, M. Fukumi, N. Akamatsu, M. Yasutomo","doi":"10.1109/ANZIIS.2001.974050","DOIUrl":null,"url":null,"abstract":"Recently, internal human organ disorders that medical image analysis can be used to detect is being actively researched. The research have however, concentrated on the extraction of pulmonary tumors. There is therefore, little research being done on the extraction of liver tumors. This is because there is no difference between concentrated values of a healthy part and one with a tumor in liver CT images. In this paper, the extraction method of such liver tumors is proposed. Furthermore, in order to demonstrate the effectiveness of the proposed scheme, we show a simulation example, using real CT image data.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANZIIS.2001.974050","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Recently, internal human organ disorders that medical image analysis can be used to detect is being actively researched. The research have however, concentrated on the extraction of pulmonary tumors. There is therefore, little research being done on the extraction of liver tumors. This is because there is no difference between concentrated values of a healthy part and one with a tumor in liver CT images. In this paper, the extraction method of such liver tumors is proposed. Furthermore, in order to demonstrate the effectiveness of the proposed scheme, we show a simulation example, using real CT image data.