@Article{Supelec673,
author = {Beatrice Chevaillier and Damien Mandry and Jean-Luc Collette and Michel Claudon and Marie-Agnès Galloy and Olivier Pietquin},
title = {{Functional segmentation of renal DCE-MRI sequences using vector quantization algorithms}},
journal = {Neural Processing Letters},
year = {2011},
volume = {34},
number = {1},
pages = {71-85},
url = {http://www.springerlink.com/content/346g446336542544/},
isbn = {1370-4621},
doi = {10.1007/s11063-011-9184-y},
abstract = {In dynamic contrast-enhanced magnetic resonance imaging,
segmentation of internal kidney structures like cortex, medulla
and cavities is essential for functional assessment.
To avoid fastidious and time-consuming manual segmentation,
semi-
automatic methods have been recently developed. Some of them
use
the differences between temporal contrast
evolution in each anatomical region to perform functional
segmentation. We test two methods where pixels are classified
according to their time-intensity evolution. They both
require a vector quantization stage with some unsupervised
learning algorithm (K-means or Growing Neural Gas with
targeting). Three or more classes are thus obtained. In the
first
case the method is completely automatic. In the second case, a
restricted intervention by an observer is required for merging.
As no ground truth is available for result evaluation,
a manual anatomical segmentation is considered as a reference.
Some discrepancy criteria like overlap, extra pixels and
similarity index are computed between this segmentation and
a functional one. The same criteria are also evaluated between
the reference and another manual segmentation. Results are
comparable for the two types of comparisons, proving that
anatomical segmentation can be performed using functional
information. }
}