Technical Section: A hybrid pixel-based classification method for blood vessel segmentation and aneurysm detection on CTA

Authors:
S. Kostopoulos;D. Glotsos;G. C. Kagadis;A. Daskalakis;P. Spyridonos;I. Kalatzis;M. Karamessini;T. Petsas;D. Cavouras;G. Nikiforidis
Affiliations:
Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 00 Rio, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 00 Rio, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 00 Rio, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 00 Rio, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 00 Rio, Greece;Medical Image and Signal Processing Laboratory, Department of Medical Instruments Technology, Technological Institute of Athens, 122 10 Athens, Greece;Department of Radiology, University Hospital of Patras, 265 00 Rio, Greece;Department of Radiology, University Hospital of Patras, 265 00 Rio, Greece;Medical Image and Signal Processing Laboratory, Department of Medical Instruments Technology, Technological Institute of Athens, 122 10 Athens, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 00 Rio, Greece
Venue:
Computers and Graphics
Year:
2007

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Abstract

In the present study, a hybrid semi-supervised pixel-based classification algorithm is proposed for the automatic segmentation of intracranial aneurysms in Computed Tomography Angiography images. The algorithm was designed to discriminate image pixels as belonging to one of the two classes: blood vessel and brain parenchyma. Its accuracy in vessel and aneurysm detection was compared with two other reliable methods that have already been applied in vessel segmentation applications: (a) an advanced and novel thresholding technique, namely the frequency histogram of connected elements (FHCE), and (b) the gradient vector flow snake. The comparison was performed by means of the segmentation matching factor (SMF) that expressed how precise and reproducible was the vessel and aneurysm segmentation result of each method against the manual segmentation of an experienced radiologist, who was considered as the gold standard. Results showed a superior SMF for the hybrid (SMF=88.4%) and snake (SMF=87.2%) methods compared to the FHCE (SMF=68.9%). The major advantage of the proposed hybrid method is that it requires no a priori knowledge of the topology of the vessels and no operator intervention, in contrast to the other methods examined. The hybrid method was efficient enough for use in 3D blood vessel reconstruction.