DocumentCode :
1674377
Title :
Liver Contour Extraction using Snake and Initial Boundary Auto-Generation
Author :
Li Ma ; Zhu, Lei
Author_Institution :
Sch. of Autom., Hangzhou Dianzi Univ., Hangzhou
fYear :
2008
Firstpage :
2669
Lastpage :
2672
Abstract :
Contour extraction of liver tissues in CT image is particularly challenging due to the anatomic complexity. An integrated model based statistical learning and active contour scheme - modified snake are presented in the paper to simplify the automatically liver contour extraction and then achieve refinements of liver boundary with accuracy. The proposed scheme consists of two sub-routines: initial contour acquisition and refinement. The former firstly extracts coarser liver regions based on estimated mixture Gaussian distribution model by the EM algorithm and then take initial contour extraction. The latter makes further refinement using modified snake algorithm with additional intensity item in external energy. Experimental results show the ability of the proposed algorithm to achieve satisfied liver boundaries in presence of liver tumor and other anatomic organs, and suggest its suitability to other medical image contour detection tasks.
Keywords :
Gaussian distribution; cancer; computerised tomography; edge detection; expectation-maximisation algorithm; initial value problems; liver; medical image processing; tumours; CT image; EM algorithm; active contour scheme; anatomic organs; initial boundary auto-generation; initial contour acquisition; integrated model; liver boundary refinements; liver contour extraction; liver tumor; medical image contour detection tasks; mixture Gaussian distribution model; modified snake algorithm; statistical learning; Abdomen; Automation; Cancer detection; Computed tomography; Data mining; Humans; Image segmentation; Liver; Parameter estimation; Statistical learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-1747-6
Electronic_ISBN :
978-1-4244-1748-3
Type :
conf
DOI :
10.1109/ICBBE.2008.999
Filename :
4535879
Link To Document :
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