Segmentation of the Liver using a 3D Statistical Shape Model

Please always quote using this URN: urn:nbn:de:0297-zib-7847
  • This paper presents an automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model. Segmentation of the liver is an important prerequisite in liver surgery planning. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between different surfaces. We propose to use a geometric approach that is based on minimizing the distortion of the correspondence mapping between two different surfaces. For the adaption of the shape model to the image data a profile model based on the grey value appearance of the liver and its surrounding tissues in contrast enhanced CT data was developed. The robustness of this method results from a previous nonlinear diffusion filtering of the image data. Special focus is turned to the quantitative evaluation of the segmentation process. Several different error measures are discussed and implemented in a study involving more than 30 livers.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Author:Hans LameckerORCiD, Thomas Lange, Martin Seebass
Document Type:ZIB-Report
Tag:3D Correspondence Problem; Diffusion Filtering; Geometric Distortion; Model-Based Segmentation; Statistical Shape Model; Surface Distance
MSC-Classification:68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area) / 68Uxx Computing methodologies and applications / 68U01 General
CCS-Classification:I. Computing Methodologies / I.4 IMAGE PROCESSING AND COMPUTER VISION (REVISED) / I.4.6 Segmentation
I. Computing Methodologies / I.5 PATTERN RECOGNITION / I.5.1 Models
Date of first Publication:2004/04/02
Series (Serial Number):ZIB-Report (04-09)