Semi-Automatic Three-Dimensional Vessel Segmentation Using a Connected Component Localization of the Region-Scalable Fitting Energy

36/2015

Semi-Automatic Three-Dimensional Vessel Segmentation Using a Connected Component Localization of the Region-Scalable Fitting Energy

Friday 10th July 2015

Fedele, M.; Faggiano, E.; Barbarotta, L.; Cremonesi, F.; Formaggia, L.; Perotto, S.

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.

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IEEE, Proceedings of the 9th International Symposium on Image and Signal Processing and Analysis, 20