Cardiac LV segmentation using a 3D active shape model driven by fuzzy inference

H.C. Assen, van, M.G. Danilouchkine, F. Behloul, H.J. Lamb, R.J. Geest, van der, J.H.C. Reiber, B.P.F. Lelieveldt

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

44 Citations (Scopus)


Manual quantitative analysis of cardiac left ventricular function using multi-slice CT is labor intensive because of the large datasets. In previous work, an intrinsically three-dimensional segmentation method for cardiac CT images was presented based on a 3D Active Shape Model (3D-ASM). This model systematically overestimated left ventricular volume and underestimated blood pool volume, due to inaccurate estimation of candidate points during the model update steps. In this paper, we propose a novel ASM candidate point generation method based on a Fuzzy Inference System (FIS), which uses image patches as an input. Visual and quantitative evaluation of the results for 7 out of 9 patients shows substantial improvement for endocardial contours, while the resulting volume errors decrease considerably (blood pool: -39±29 cubic voxels in the previous model, -0.66±6.2 cubic voxels in the current). Standard deviation of the epicardial volume decreases by approximately 50%.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2003 : 6th International Conference, Montréal, Canada, November 15-18, 2003. Proceedings
EditorsR.E. Ellis, M. Peters
Place of PublicationBerlin
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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