1 research outputs found
BGrowth: an efficient approach for the segmentation of vertebral compression fractures in magnetic resonance imaging
Segmentation of medical images is a critical issue: several process of
analysis and classification rely on this segmentation. With the growing number
of people presenting back pain and problems related to it, the automatic or
semi-automatic segmentation of fractured vertebral bodies became a challenging
task. In general, those fractures present several regions with non-homogeneous
intensities and the dark regions are quite similar to the structures nearby.
Aimed at overriding this challenge, in this paper we present a semi-automatic
segmentation method, called Balanced Growth (BGrowth). The experimental results
on a dataset with 102 crushed and 89 normal vertebrae show that our approach
significantly outperforms well-known methods from the literature. We have
achieved an accuracy up to 95% while keeping acceptable processing time
performance, that is equivalent to the state-of-the-artmethods. Moreover,
BGrowth presents the best results even with a rough (sloppy) manual annotation
(seed points).Comment: This is a pre-print of an article published in Symposium on Applied
Computing. The final authenticated version is available online at
https://doi.org/10.1145/3297280.329972