Segmentation of magnetic resonance images in presence of severe intensity inhomogeneities

In high-field whole body magnetic resonance imaging (MRI), images usually suffer from intensity inhomogeneities. The BC-FAT (bias correction by fitting of adipose tissue intensity) algorithm can compensate for this; however, it is limited to images containing only one object, e.g. the torso. In this...

Full description

Saved in:
Bibliographic Details
Main Authors: Liebgott, Florian, Wurslin, Christian, Bin Yang
Format: Conference Proceeding
Language:English
Subjects:
Active contours
automatic image segmentation
Clustering algorithms
Hough transform
Image edge detection
Image segmentation
k-means clustering
Magnetic resonance imaging
Nonhomogeneous media
Transforms
whole body MRI
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In high-field whole body magnetic resonance imaging (MRI), images usually suffer from intensity inhomogeneities. The BC-FAT (bias correction by fitting of adipose tissue intensity) algorithm can compensate for this; however, it is limited to images containing only one object, e.g. the torso. In this paper, we present a method, which extends the BC-FAT algorithm to images containing multiple objects and thus to cross-sectional images of the whole body. This is achieved by an algorithm for the robust and fully automated object detection in MR images using the Hough transform and a modified k-means clustering. We also present a two-scale approach for active contours in order to eliminate the need of object size dependent parametrization for BC-FAT.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2013.6637802