Share Email Print

Proceedings Paper

A pattern recognition framework for vessel segmentation in 4D CT of the brain
Author(s): J. J. Mordang; M. T. H. Oei; R. van den Boom; E. J. Smit; M. Prokop; B. van Ginneken; R. Manniesing
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In this study, a pattern recognition-based framework is presented to automatically segment the complete cerebral vasculature from 4D Computed Tomography (CT) patient data. Ten consecutive patients whom were admitted to our hospital on a suspicion of ischemic stroke were included in this study. A background mask and bone mask were calculated based on intensity thresholding and morphological operations, and the following six image features were proposed: 1) a subtraction image of a subtraction image consisting of timing-invariant CTA and non-constrast CT, 2) the area under the curve of a gamma variate function fitted to the tissue curves, 3-5) three optimized parameter values of this gamma variate function, and 6) a vessel likeliness function. After masking bone and background, these features were used to train a linear discriminant voxel classifier (LDC) on regions of interest (ROIs), which were annotated in soft tissue (white matter and gray matter) and vessels by an expert observer. The LDC was trained in a leave-one-out manner in which 9 patients tissue ROIs were used for training and the remaining patient tissue ROIs were used for testing the classifier. To evaluate the frame work, for each training cycle the accuracy was calculated by dividing the true positives and negatives by the true positives and negatives and false positives and negatives. The resulting averaged accuracy was 0:985±0:014 with a range of 0:957 to 0:999.

Paper Details

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866919 (13 March 2013); doi: 10.1117/12.2006824
Show Author Affiliations
J. J. Mordang, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
M. T. H. Oei, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
R. van den Boom, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
E. J. Smit, Univ. Medical Ctr. Utrecht (Netherlands)
M. Prokop, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
B. van Ginneken, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
R. Manniesing, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?