NDLI: Anatomical object recognition and labeling by atlas-based focused non-rigid registration and region-growing

Content Provider	IEEE Xplore Digital Library
Author	Leiguang Gong Rohrer, J. Iyengar, G. Butler, B. Lumsden, A.
Copyright Year	2008
Description	Author affiliation: IBM T.J. Watson Res. Center, Yorktown, NY (Leiguang Gong)
Abstract	Computer-assisted recognition of anatomical objects in medical images is at the center of many important clinical applications. Automatic extraction and recognition of human abdominal structures from CT images has been particularly challenging for medical imaging research and applications. Intra-patient and inter-patient spatial, morphological and intensity variability is typically significantly contributing to great difficulties in developing satisfactory automatic solutions to the problem. In this paper we report on a new approach, which treats recognition of anatomical objects from a given medical image as a task of non-rigid registration followed by segmentation. It uses the knowledge inferred from an atlas or model image to specify a sequence of smaller sub-image spaces or spatial contexts to register progressively the atlas image with the given patient image. The labels of the target objects in the atlas image are carried over to the patient image by the registration process representing the recognition result, which is further improved by a region-growing process. Preliminary experiments of artery blood vessel recognition and labeling with real patient data have demonstrated the potential of the method to be a viable alternative solution to the problem.
Starting Page	1354
Ending Page	1358
File Size	193720
Page Count	5
File Format	PDF
ISBN	9781424417230
DOI	10.1109/ICALIP.2008.4590199
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Computed tomography Blood vessels Circuit faults Pixel Biomedical imaging Fault currents
Content Type	Text
Resource Type	Article

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