Capturing large shape variations of liver using population-based statistical shape models

  • Amir H. Foruzan
  • Yen-Wei Chen
  • Masatoshi Hori
  • Yoshinobu Sato
  • Noriyuki Tomiyama
Original Article



Statistical shape models (SSMs) represent morphological variations of a specific object. When there are large shape variations, the shape parameters constitute a large space that may include incorrect parameters. The human liver is a non-rigid organ subject to large deformations due to external forces or body position changes during scanning procedures. We developed and tested a population-based model to represent the shape of liver.


Upper abdominal CT-scan input images are represented by a conventional shape model. The shape parameters of individual livers extracted from the CT scans are employed to classify them into different populations. Corresponding to each population, an SSM model is built. The liver surface parameter space is divided into several subspaces which are more compact than the original space. The proposed model was tested using 29 CT-scan liver image data sets. The method was evaluated by model compactness, reconstruction error, generality and specificity measures.


The proposed model is implemented and tested using CT scans that included liver shapes with large shape variations. The method was compared with conventional and recently developed shape modeling methods. The accuracy of the proposed model was nearly twice that achieved with the conventional model. The proposed population-based model was more general compared with the conventional model. The mean reconstruction error of the proposed model was 0.029 mm while that of the conventional model was 0.052 mm.


A population-based model to represent the shape of liver was developed and tested with favorable results. Using this approach, the liver shapes from CT scans were modeled by a more compact, more general, and more accurate model.


Statistical shape model Population-based shape model  Shape representation Medical image analysis 


Conflict of interest

Amir H. Foruzan, Yen-Wei Chen, Masatoshi Hori, Yoshinobu Sato and Noriyuki Tomiyama declare that they have no conflict of interest.

Supplementary material

11548_2014_1000_MOESM1_ESM.pdf (161 kb)
Supplementary material 1 (pdf 161 KB)


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Copyright information

© CARS 2014

Authors and Affiliations

  • Amir H. Foruzan
    • 1
    • 2
  • Yen-Wei Chen
    • 2
  • Masatoshi Hori
    • 3
  • Yoshinobu Sato
    • 4
  • Noriyuki Tomiyama
    • 3
  1. 1.Department of Biomedical Engineering, Engineering FacultyShahed UniversityTehranIran
  2. 2.Intelligent Image Processing Lab, College of Information Science and EngineeringRitsumeikan UniversityShigaJapan
  3. 3.Department of Radiology, Graduate School of MedicineOsaka UniversityOsakaJapan
  4. 4.Division of Image Analysis, Graduate School of MedicineOsaka UniversityOsakaJapan

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