{"title":"乳房超声成像中复杂形状夹杂物的可视化问题,图像处理","authors":"V. Filatova, Valeria A. Sedaikina","doi":"10.1109/DD49902.2020.9274618","DOIUrl":null,"url":null,"abstract":"Visualization of the acoustical medium is an important diagnostic instrument for determining breast cancer. The acoustical medium is visualized using the well-known geophysical method — reverse time migration (RTM). We describe some results of simulations of this problem for a specific breast model in 2D. The numerical solution of the problem relies on a standard library of parallel computing (OpenMP) and is carried out on a cluster system. After applying the RTM procedure, the images are processed for localizing regions of interests (ROIs). The procedure of processing includes increasing the intensity of the inclusions that interest us, filtering to reduce and remove noise, edge detection of the inclusions. The described method is automated to eliminate the need for manual processing.","PeriodicalId":133126,"journal":{"name":"2020 Days on Diffraction (DD)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Visualization of complex shape inclusions in the breast ultrasound tomography problem, image processing\",\"authors\":\"V. Filatova, Valeria A. Sedaikina\",\"doi\":\"10.1109/DD49902.2020.9274618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Visualization of the acoustical medium is an important diagnostic instrument for determining breast cancer. The acoustical medium is visualized using the well-known geophysical method — reverse time migration (RTM). We describe some results of simulations of this problem for a specific breast model in 2D. The numerical solution of the problem relies on a standard library of parallel computing (OpenMP) and is carried out on a cluster system. After applying the RTM procedure, the images are processed for localizing regions of interests (ROIs). The procedure of processing includes increasing the intensity of the inclusions that interest us, filtering to reduce and remove noise, edge detection of the inclusions. The described method is automated to eliminate the need for manual processing.\",\"PeriodicalId\":133126,\"journal\":{\"name\":\"2020 Days on Diffraction (DD)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Days on Diffraction (DD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DD49902.2020.9274618\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Days on Diffraction (DD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DD49902.2020.9274618","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visualization of complex shape inclusions in the breast ultrasound tomography problem, image processing
Visualization of the acoustical medium is an important diagnostic instrument for determining breast cancer. The acoustical medium is visualized using the well-known geophysical method — reverse time migration (RTM). We describe some results of simulations of this problem for a specific breast model in 2D. The numerical solution of the problem relies on a standard library of parallel computing (OpenMP) and is carried out on a cluster system. After applying the RTM procedure, the images are processed for localizing regions of interests (ROIs). The procedure of processing includes increasing the intensity of the inclusions that interest us, filtering to reduce and remove noise, edge detection of the inclusions. The described method is automated to eliminate the need for manual processing.