Knowledge-based interpretation of MR brain images

• Published in: IEEE transactions on medical imaging Vol. 15; no. 4; pp. 443 - 452
• Main Authors: Sonka, M., Tadikonda, S.K., Collins, S.M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.1996; Institute of Electrical and Electronics Engineers
• Subjects: Biological and medical sciences; Brain; Computer errors; Computerized, statistical medical data processing and models in biomedicine; Feedback loop; Genetic algorithms; Image generation; Image segmentation; Labeling; Magnetic resonance; Medical computing and teaching; Medical sciences; Optimization methods; Testing; Performance evaluation; Human; Error analysis; Segmentation; Image interpretation; Spin lattice relaxation; Optimization method; Central nervous system; Labelling; Knowledge base; Experimental study; Nuclear magnetic resonance imaging; Biomedical data processing; Genetic algorithm; Brain (vertebrata)
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.511748

The authors have developed a method for fully automated segmentation and labeling of 17 neuroanatomic structures such as thalamus, caudate nucleus, ventricular system, etc. in magnetic resonance (MR) brain images. The authors' method is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimization technique to generate and evaluate image interpretation hypotheses in a feedback loop. The authors' method was trained in 20 individual T1-weighted MR images. Observer-defined contours of neuroanatomic structures were used as a priori knowledge. The method's performance was validated in eight MR images by comparison to observer-defined independent standards. The GA-based image interpretation method correctly interpreted neuroanatomic structures in all images from the test set. Computer-identified and observer-defined neuroanatomic structure areas correlated very well (r=0.99, y=0,95x-2.1). Border positioning errors were small, with a root mean square (rms) border positioning error of 1.5/spl plusmn/0.6 pixels. The authors' GA-based image interpretation method represents a novel approach to image interpretation and has been shown to produce accurate labeling of neuroanatomic structures in a set of MR brain images.