Codice | 36/2015 |
Titolo | Semi-Automatic Three-Dimensional Vessel Segmentation Using a Connected Component Localization of the Region-Scalable Fitting Energy |
Data | 2015-07-10 |
Autore/i | Fedele, M.; Faggiano, E.; Barbarotta, L.; Cremonesi, F.; Formaggia, L.; Perotto, S. |
Link | Download full text | Pubblicato | IEEE, Proceedings of the 9th International Symposium on Image and Signal Processing and Analysis, 20 |
Abstract | Segmentation of patient-specific vascular segments of interest from
medical images is an important topic for numerous applications. De-
spite the great importance of having semi-automatic segmentation meth-
ods in this field, the process of image segmentation is still based on
several operator-dependent steps which make large-scale segmentation
a non trivial and time consuming task. In this work we present a
semi-automatic segmentation method to reconstruct vascular struc-
tures from three-dimensional medical images. We start from the mini-
mization of the Region Scalable Fitting Energy using the Split-Bregman
method and we modify the resulting algorithm adding a connected
component extraction of the solution starting from a point that identi-
fies the vascular structure of interest. In this way, we add a constraint
to the algorithm focusing it only on the vascular structure we want
to reconstruct and avoiding the attachment with the nearby objects.
Finally, we describe a strategy to minimize the number of involved
parameters in order to limit the user effort. The results obtained on
two different images (a Magnetic Resonance and a Computed Tomog-
raphy) demonstrate that our method outperforms the original method
in segmenting the vascular region of interest without the inclusion of
nearby objects in the result. |
|