Watershed and multimodal data for brain vessel segmentation: Application to the superior sagittal sinus

Authors:
N. Passat;C. Ronse;J. Baruthio;J. -P. Armspach;J. Foucher
Affiliations:
LSIIT, Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection, UMR 7005 CNRS/ULP, Strasbourg 1 University, Parc d'Innovation, Bd Sébastien Brant BP 10413 ...;LSIIT, Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection, UMR 7005 CNRS/ULP, Strasbourg 1 University, Parc d'Innovation, Bd Sébastien Brant BP 10413 ...;IPB-LNV, Institut de Physique Biologique, Laboratoire de Neuroimagerie in Vivo, UMR 7004 CNRS/ULP, Strasbourg 1 University, Hôpital Civil, 4 rue Kirschleger, F-67085 Strasbourg Cedex, France;IPB-LNV, Institut de Physique Biologique, Laboratoire de Neuroimagerie in Vivo, UMR 7004 CNRS/ULP, Strasbourg 1 University, Hôpital Civil, 4 rue Kirschleger, F-67085 Strasbourg Cedex, France;Unité de Recherche en Psychopathologie et Pharmacologie de la Cognition, U405 INSERM, Hôpital Civil, 1 place de l'Hôpital, BP 426, F-67091 Strasbourg Cedex, France
Venue:
Image and Vision Computing
Year:
2007

Citing 6
Cited 1

A Boolean characterization of three-dimensional simple points

Pattern Recognition Letters
Liver Blood Vessels Extraction by a 3-D Topological Approach

MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Symbolic Description of 3-D Structures Applied to Cerebral Vessel Tree Obtained from MR Angiography Volume Data

IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
A review of vessel extraction techniques and algorithms

ACM Computing Surveys (CSUR)
Using multimodal MR data for segmentation and topology recovery of the cerebral superficial venous tree

ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II

IEEE Transactions on Information Technology in Biomedicine

Segmentation of 4D cardiac MRI: Automated method based on spatio-temporal watershed cuts

Image and Vision Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Magnetic resonance angiography (MRA) provides three-dimensional data of vascular structures by visualising the flowing blood signal. Algorithms dedicated to vessel segmentation generally detect the cerebral vascular tree by only seeking this high intensity blood signal in MRA data. The method presented in this paper proposes a different strategy which consists in using both MRA and classical MRI in order to integrate a priori anatomical knowledge for guidance of the vessel segmentation process. It then uses mathematical morphology tools to carry out a simultaneous segmentation of both blood signal in MRA and blood and wall signal in MRI, enabling to take advantage of a larger amount of information than previously proposed methods. This method is dedicated to the superior sagittal sinus segmentation; however, similar strategies could be considered for segmentation of other vascular structures. It has been performed on a database composed of 9 couples of MRA and MRI, providing results which have been validated and compared to other ones obtained with a region-growing algorithm. Their validation tends to prove that the proposed method is reliable even when the vascular signal is inhomogeneous or contains artifacts.