A 2D Fourier Approach to Deformable Model Segmentation of 3D Medical Images
Abstract
Anatomical shapes present a unique problem in terms of accurate representation and medical image segmentation. Three-dimensional statistical shape models have been extensively researched as a means of autonomously segmenting and representing models. We present a segmentation method driven by a statistical shape model based on a priori shape information from manually segmented training image sets. Our model is comprised of a stack of two-dimensional Fourier descriptors computed from the perimeters of the segmented training image sets after a transformation into a canonical coordinate frame. We apply our shape model to the segmentation of CT and MRI images of the distal femur via an original iterative method based on active contours. The results from the application of our novel method demonstrate its ability to accurately capture anatomical shape variations and guide segmentation. Our quantitative results are unique in that most similar previous work presents only qualitative results.
Cite
Text
Berg et al. "A 2D Fourier Approach to Deformable Model Segmentation of 3D Medical Images." European Conference on Computer Vision, 2004. doi:10.1007/978-3-540-27816-0_16Markdown
[Berg et al. "A 2D Fourier Approach to Deformable Model Segmentation of 3D Medical Images." European Conference on Computer Vision, 2004.](https://mlanthology.org/eccv/2004/berg2004eccv-d/) doi:10.1007/978-3-540-27816-0_16BibTeX
@inproceedings{berg2004eccv-d,
title = {{A 2D Fourier Approach to Deformable Model Segmentation of 3D Medical Images}},
author = {Berg, Eric and Mahfouz, Mohamed and Debrunner, Christian and Hoff, William A.},
booktitle = {European Conference on Computer Vision},
year = {2004},
pages = {181-192},
doi = {10.1007/978-3-540-27816-0_16},
url = {https://mlanthology.org/eccv/2004/berg2004eccv-d/}
}