DocumentCode :
686702
Title :
Low dose CT image restoration using a localized patch database
Author :
Sungsoo Ha ; Mueller, Klaus
Author_Institution :
Comput. Sci. Dept., Stony Brook Univ., Stony Brook, NY, USA
fYear :
2013
fDate :
Oct. 27 2013-Nov. 2 2013
Firstpage :
1
Lastpage :
2
Abstract :
As growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases, the low-dose CT imaging is becoming a hot issue, how to minimize the radiation exposure level while maintaining the diagnostic performance. In previous work, we have proposed a framework that is working in conjunction with image-based database. It restores a low-dose image with modified non-local means (NLM) that have expanded search window to include regular-dose priors obtained from same or different patients. In this paper, we further develop the framework to know the internal structures of CT images so that the NLM filtering is applied with more intelligent way. In addition, knowing the internal structures changes the database from image-based to patch-based that organized structure by structure. As preliminary work, this was tested to restore a structure of low-dose CT image and it showed a very promising results.
Keywords :
cancer; computerised tomography; dosimetry; filtering theory; genetics; image restoration; medical image processing; visual databases; NLM filtering; cancerous diseases; computerised tomography; diagnostic performance; genetic diseases; image-based database; localized patch database; low dose CT image restoration; nonlocal means method; radiation exposure level minimization; radiation induced diseases; regular-dose priors; Computed tomography; Databases; Filtering; Image quality; Image reconstruction; Image restoration; Lungs;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2013 IEEE
Conference_Location :
Seoul
Print_ISBN :
978-1-4799-0533-1
Type :
conf
DOI :
10.1109/NSSMIC.2013.6829131
Filename :
6829131
Link To Document :
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