Segmentation of inversion recovery MR images using neural networks: a study on aging

John O. Glass; Wilburn E. Reddick; Virginia S. Yo; R. Grant Steen

doi:10.1117/12.310938

24 June 1998 Segmentation of inversion recovery MR images using neural networks: a study on aging

John O. Glass, Wilburn E. Reddick, Virginia S. Yo, R. Grant Steen

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310938
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

Clinicians have long desired early detection of neurological abnormality for treatment of brain malignancies. In attempts to address this concern, there are numerous reports publishing normative databases of age-related changes of the brain in healthy controls, many using magnetic resonance imaging (MRI). However, most of the method used to access tissue volumes were subject to observer variability. We developed a Kohonen self-organizing map to automatically segment MR images for reproducible and accurate identification of tissues. The developed method was applied to quantitatively assess subtle volume differences in normal controls due to maturational and degenerative changes. The volumes calculated in the test population of 73 controls agreed with current hypothesizes concerning age-related changes of the brain as determined by linear regression analysis of segmented tissue to age. Percent gray matter and percent white matter, as well as the ratio of gray matter to white matter were all found to be significantly correlated with age. Percent gray matter and the ratio of gray matter to white matter were inversely proportional to age while percent white matter was directly proportional to age. These results suggest the utility of the developed segmentation technique, as well as the clinical application it may hold.

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John O. Glass, Wilburn E. Reddick, Virginia S. Yo, and R. Grant Steen "Segmentation of inversion recovery MR images using neural networks: a study on aging", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310938

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KEYWORDS

Image segmentation

Tissues

Brain

Magnetic resonance imaging

Neurons

Image processing algorithms and systems

Image classification

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