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Magnetic resonance image segmentation using pattern recognition, and applied to image registration and quantitation
Author(s) -
Saeed N.
Publication year - 1998
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/(sici)1099-1492(199806/08)11:4/5<157::aid-nbm528>3.0.co;2-l
Subject(s) - artificial intelligence , segmentation , magnetic resonance imaging , pattern recognition (psychology) , histogram , image segmentation , computer science , computer vision , identification (biology) , image processing , image registration , image (mathematics) , medicine , radiology , biology , botany
This review highlights various magnetic resonance image (MRI) segmentation algorithms that employ pattern recognition. The procedures are grouped into two categories: low‐ to intermediate‐level, and high‐level image processing. The former consists of grey level histogram analysis, texture definition, edge identification, region growing, and contour following. The roles of significant prior knowledge, neural networks and cluster analysis are examined by producing objective identification of anatomical structures. The application of the segmented anatomical structures in image registration, to monitor the disease progression or growth of anatomy in normal volunteers and patients, is highlighted. The use of the segmented anatomy in measuring volumes of structures in normals and patients is also examined.