Premium
A Logic Programming and Expert Statistical Systems Approach for Tissue Characterization in Magnetic Resonance Imaging
Author(s) -
Levy G. C.,
Dudewicz E. J.,
Harner T. J.,
Dudewicz Edward J.,
Wehrli F. W.,
Breger R.
Publication year - 1989
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710310202
Subject(s) - white matter , computer science , magnetic resonance imaging , artificial intelligence , software , feature (linguistics) , gray (unit) , linear discriminant analysis , pattern recognition (psychology) , nuclear medicine , radiology , medicine , programming language , linguistics , philosophy
The main research goal has been to evaluate significant factors affecting the in vivo magnetic resonance imaging (MRI) parameters T , T , T 2 , and 1 H density. This approach differs significantly from other such projects in that the experimental data analysis is being performed while concurrently developing automated, computer‐aided analysis software for such MRI tissue parameters. In the experimental portion of the project, statistical analyses, and a heuristic minimum/maximum discriminant analysis algorithm have been explored. Both methods have been used to classify tissue types from 1.5 Tesla transaxial MR images of the human brain. The developing program, written in the logic programming language Prolog, is similar in a number of ways to many existing expert systems now in use for other medical applications; inclusion of the underlying statistical data base and advanced statistical analyses is the main differentiating feature of the current approach. First results indicate promising classification accuracy of various brain tissues such as gray and white matter, as well as differentiation of different types of gray matter and white matter (e.g.: caudate‐nucleus vs. thalamus, both representatives of gray matter; and, cortical white matter vs. internal capsule as representative of white matter). Taking all four tissue types together, the percentage of correct classifications ranges from 73 to 87%.