Document Detail


Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation.
MedLine Citation:
PMID:  18550045     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Identifying liver region from abdominal computed tomography-angiography (CTA) data sets is one of the essential steps in evaluation of transplantation donors prior to the hepatic surgery. However, due to gray level similarity of adjacent organs, injection of contrast media and partial volume effects; robust segmentation of the liver is a very difficult task. Moreover, high variations in liver margins, different image characteristics with different CT scanners and atypical liver shapes make the segmentation process even harder. In this paper, we propose a three stage (i.e. pre-processing, classification, post-processing); automatic liver segmentation algorithm that adapts its parameters according to each patient by learning the data set characteristics in parallel to segmentation process to address all the challenging aspects mentioned above. The efficiency in terms of the time requirement and the overall segmentation performance is achieved by introducing a novel modular classification system consisting of a K-Means based simple classification system and an MLP based complex one which are combined with a data-dependent and automated switching mechanism that decides to apply one of them. Proposed approach also makes the design of the overall classification system fully unsupervised that depends on the given CTA series only without requiring any given training set of CTA series. The segmentation results are evaluated by using area error rate and volume calculations and the success rate is calculated as 94.91% over a data set of diverse CTA series of 20 patients according to the evaluation of the expert radiologist. The results show that, the proposed algorithm gives better results especially for atypical liver shapes and low contrast studies where several algorithms fail.
Authors:
M Alper Selver; Aykut Kocaoğlu; Güleser K Demir; Hatice Doğan; Oğuz Dicle; Cüneyt Güzeliş
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-06-11
Journal Detail:
Title:  Computers in biology and medicine     Volume:  38     ISSN:  0010-4825     ISO Abbreviation:  Comput. Biol. Med.     Publication Date:  2008 Jul 
Date Detail:
Created Date:  2008-06-27     Completed Date:  2008-09-11     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1250250     Medline TA:  Comput Biol Med     Country:  United States    
Other Details:
Languages:  eng     Pagination:  765-84     Citation Subset:  IM    
Affiliation:
Electrical and Electronics Engineering Department, Dokuz Eylül University, Buca, Izmir 35160, Turkey. alper.selver@deu.edu.tr
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Automation*
Humans
Liver / pathology
Liver Transplantation*
Tomography, X-Ray Computed

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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