Conference proceeding
Fast globally optimal single surface segmentation using regional properties
Proceedings of SPIE, Vol.7623(1), pp.76231O-76231O-9
Medical Imaging 2010: Image Processing
03/05/2010
DOI: 10.1117/12.844397
Abstract
Efficient segmentation of globally optimal surfaces in volumetric images is a central problem in many medical image
analysis applications. Intra-class variance has been successfully utilized, for instance, in the Chan-Vese model especially
for images without prominent edges. In this paper, we study the optimization problem of detecting a region (volume)
bounded by a smooth terrain-like surface, whose intra-class variance is minimized. A novel polynomial time algorithm is
developed. Our algorithm is based on the shape probing technique in computational geometry and computes a sequence
of
maximum flows in the derived graphs, where
is the size of the input image. Our further investigation shows
that those
graphs form a monotone parametric flow network, which enables to solving the optimal region detection
problem in the complexity of computing a single maximum flow. The method has been validated on computer-synthetic
volumetric images. Its applicability to clinical data sets was demonstrated on 20 3-D airway wall CT images from 6
subjects. The achieved results were highly accurate. The mean unsigned surface positioning error of outer walls of the
tubes is 0.258 ± 0.297mm, given a voxel size of 0.39 x 0.39 x 0.6mm
.
Details
- Title: Subtitle
- Fast globally optimal single surface segmentation using regional properties
- Creators
- Xin Dou - University of IowaXiaodong Wu - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of SPIE, Vol.7623(1), pp.76231O-76231O-9
- Conference
- Medical Imaging 2010: Image Processing
- DOI
- 10.1117/12.844397
- ISSN
- 0277-786X
- Language
- English
- Date published
- 03/05/2010
- Academic Unit
- The Iowa Institute for Biomedical Imaging; Electrical and Computer Engineering; Radiation Oncology
- Record Identifier
- 9984197274902771
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