%0 Conference Proceedings
%T Evaluation of Carotid Artery Segmentation with Centerline Detection and Active Contours without Edges Algorithm
%+ Pedagogical University of Krakow
%+ AGH University of Science and Technology [Krakow, PL] (AGH UST)
%A Hachaj, Tomasz
%A Ogiela, Marek, R.
%Z Part 2: Workshop
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B International Cross-Domain Conference and Workshop on Availability, Reliability, and Security (CD-ARES)
%C Prague, Czech Republic
%Y Gerald Quirchmayr
%Y Josef Basl
%Y Ilsun You
%Y Lida Xu
%Y Edgar Weippl
%I Springer
%3 Multidisciplinary Research and Practice for Information Systems
%V LNCS-7465
%P 468-478
%8 2012-08-20
%D 2012
%R 10.1007/978-3-642-32498-7_35
%K Active contours without edges
%K lumen segmentation
%K carotid bifurcation
%K computed tomography angiography
%K brain perfusion maps
%K computer - aided diagnosis
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X The main contribution of this article is a new method of segmentation of carotid artery based on original authors inner path finding algorithm and active contours without edges segmentation method for vessels wall detection. Instead of defining new force to being minimized or intensity metric we decide to find optimal weight of image – dependent forces. This allows our method to be easily reproduced and applied in other software solutions. We judge the quality of segmentation by dice coefficient between manual segmentation done by a specialist and automatic segmentation performed by our algorithm. We did not find any other publication in which such approach for carotid artery bifurcation region segmentation has been proposed or investigated. The proposed algorithm has shown to be reliable method for that task. The dice coefficient at the level of 0.949(0.050 situates our algorithm among best state of the art methods for those solutions. That type of segmentation is the main step performed before sophisticated semantic analysis of complex image patterns utilized by cognitive image and scene understanding methods. The complete diagnostic record (Electronic Health Record – EHR) obtained that way consists private biometric data and its safety is essential for personal and homeland security.
%G English
%Z TC 5
%Z TC 8
%Z WG 8.4
%Z WG 8.9
%2 https://inria.hal.science/hal-01542461/document
%2 https://inria.hal.science/hal-01542461/file/978-3-642-32498-7_35_Chapter.pdf
%L hal-01542461
%U https://inria.hal.science/hal-01542461
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC8
%~ IFIP-CD-ARES
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-LNCS-7465