Document Detail


Profiling the features of pre-segmented healthy liver CT scans: Towards fast detection of liver lesions in emergency scenario.
MedLine Citation:
PMID:  22255503     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Automating the detection of lesions in liver CT scans requires a high performance and robust solution. With CT-scan start to become the norm in emergency department, the need for a fast and efficient liver lesions detection method is arising. In this paper, we propose a fast and evolvable method to profile the features of pre-segmented healthy liver and use it to detect the presence of liver lesions in emergency scenario. Our preliminary experiment with the MICCAI 2007 grand challenge datasets shows promising results of a fast training time, ability to evolve the produced healthy liver profiles, and accurate detection of the liver lesions. Lastly, the future work directions are also presented.
Authors:
Muhammad Fermi Pasha; Kee Siew Hong; Mandava Rajeswari
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference     Volume:  2011     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2011 Aug 
Date Detail:
Created Date:  2012-01-18     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  5169-73     Citation Subset:  IM    
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