Paper Info
Title
Rapid and Automated Extraction of the Fourth Ventricle from MR Images
Abstract
This paper describes a rapid and automated method that extracts the fourth ventricle from MR brain images in normal and pathological subjects. Anatomical knowledge of fourth ventricle has been incorporated into the method to define a region of interest (ROI), determine intensity thresholds in the histogram of ROI, locate the starting point for the 3D region growing, and extract all parts of the fourth ventricle and aqueduct (even if they are disconnected on images as well as to avoid inclusion of non-ventricular cerebrospinal fluid regions). The method was validated qualitatively on 30 MRI studies with variable parameters. The best overlap metric between a radiology expert and the method was 0.9988 and the worst 0.9621 on 10 quantitative studies. The mean and standard deviation of overlap metric were 0.9806 and 0.0105.
Year
DOI
Venue
2003
10.1007/978-3-540-39903-2_78
Lecture Notes in Computer Science
Keywords
Field
DocType
region growing,standard deviation,region of interest,brain imaging
Computer vision,Histogram,Pattern recognition,Computer science,Fourth ventricle,Region growing,Artificial intelligence,Region of interest,Standard deviation
Conference
Volume
ISSN
Citations 
2879
0302-9743
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Yan Xia1182.15
Aamer Aziz2445.57
Qingmao Hu316019.73
Wieslaw Lucjan Nowinski47712.08