Title
Rapid image recognition of body parts scanned in computed tomography datasets.
Abstract
Automatic CT dataset classification is important to efficiently create reliable database annotations, especially when large collections of scans must be analyzed.An automated segmentation and labeling algorithm was developed based on a fast patient segmentation and extraction of statistical density class features from the CT data. The method also delivers classifications of image noise level and patient size. The approach is based on image information only and uses an approximate patient contour detection and statistical features of the density distribution. These are obtained from a slice-wise analysis of the areas filled by various materials related to certain density classes and the spatial spread of each class. The resulting families of curves are subsequently classified using rules derived from knowledge about features of the human anatomy.The method was successfully applied to more than 5,000 CT datasets. Evaluation was performed via expert visual inspection of screenshots showing classification results and detected characteristic positions along the main body axis. Accuracy per body region was very satisfactory in the trunk (lung/liver >99.5% detection rate, presence of abdomen >97% or pelvis >95.8%) improvements are required for zoomed scans.The method performed very reliably. A test on 1,860 CT datasets collected from an oncological trial showed that the method is feasible, efficient, and is promising as an automated tool for image post-processing.
Year
DOI
Venue
2010
10.1007/s11548-009-0403-1
Int. J. Computer Assisted Radiology and Surgery
Keywords
Field
DocType
classification · computed tomography · anatomy · body region · database · segmentation · content-based image retrieval,visual inspection,computed tomography,image recognition
Computer vision,Automatic image annotation,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Computed tomography,Content-based image retrieval
Journal
Volume
Issue
ISSN
5
5
1861-6429
Citations 
PageRank 
References 
3
0.48
3
Authors
6
Name
Order
Citations
PageRank
Volker Dicken121819.02
B Lindow230.48
L Bornemann330.48
J. Drexl451.40
A Nikoubashman551.20
H O Peitgen625223.00