Automated quantification of brain magnetic resonance image hyperintensities using hybrid clustering and knowledge-based methods

✍ Scribed by Karen M. Gosche; Robert P. Velthuizen; F. Reed Murtagh; John A. Arrington; William W. Gross; James A. Mortimer; Laurence P. Clarke

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 435 KB
• Volume: 10
• Category: Article
• ISSN: 0899-9457
• DOI: 10.1002/(sici)1098-1098(1999)10:3<287::aid-ima9>3.0.co;2-z

✦ Synopsis

Previous computerized methods of hyperintensity identification in brain magnetic resonance images (MRI) either rely heavily on human intervention or on simple thresholding techniques. Such methods can lead to considerable variation in the quantification of brain hyperintensities depending upon image parameters such as contrast. This paper describes an automated, knowledge-guided method of hyperintensity detection in brain MRI that addresses problems associated with human subjectivity and thresholding techniques. This method, which we call knowledge-guided hyperintensity detection (KGHID), uses encoded knowledge of brain anatomy and MRI characteristics of individual tissues to reclassify pixels from an initial unsupervised segmentation. With this encoded knowledge, KGHID discriminates lesions embedded within the white matter, hyperintense lesions of the basal ganglia and the periventricular ring. The method is designed for high sensitivity detection and monitoring of subtle lesions in patients with neurodegenerative diseases.