Liver Segmentation Approach Using Graph Cuts and Iteratively Estimated Shape and Intensity Constrains

  • Ahmed Afifi
  • Toshiya Nakaguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7511)


In this paper, we present a liver segmentation approach. In which, the relation between neighboring slices in CT images is utilized to estimate shape and statistical information of the liver. This information is then integrated with the graph cuts algorithm to segment the liver in each CT slice. This approach does not require prior models construction, and it uses single phase CT images; even so, it is talented to deal with complex shape and intensity variations. Moreover, it eliminates the burdens associated with model construction like data collection, manual segmentation, registration, and landmark correspondence. In contrast, it requires a low user interaction to determine the liver landmarks on a single CT slice only. The proposed approach has been evaluated on 10 CT images with several liver abnormalities, including tumors and cysts, and it achieved high average scores of 81.7 using MICCAI-2007 Grand Challenge scoring system. Compared to contemporary approaches, our approach requires significantly less interaction and processing time.


Statistical Shape Model Liver Object Liver Segmentation Current Slice Liver Shape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ahmed Afifi
    • 1
  • Toshiya Nakaguchi
    • 2
  1. 1.Faculty of Computers and InformationMenoufia UniversityEgypt
  2. 2.Graduate School of EngineeringChiba UniversityJapan

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