An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces

• Published in: IEEE transactions on medical imaging Vol. 23; no. 2; pp. 246 - 255
• Main Authors: Descombes, X., Kruggel, F., Wollny, G., Gertz, H.J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Arteries; Brain - pathology; Brain Diseases - diagnosis; Central Nervous System Cysts - diagnosis; Humans; Image Interpretation, Computer-Assisted - methods; Image segmentation; Lesions; Magnetic Resonance Imaging; Monte Carlo methods; Pattern Recognition, Automated; Process design; Radiometry; Reproducibility of Results; Senior citizens; Sensitivity and Specificity; Shape; Signal Processing, Computer-Assisted
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2003.823061

This paper is concerned with the detection of multiple small brain lesions from magnetic resonance imaging (MRI) data. A model based on the marked point process framework is designed to detect Virchow-Robin spaces (VRSs). These tubular shaped spaces are due to retraction of the brain parenchyma from its supplying arteries. VRS are described by simple geometrical objects that are introduced as small tubular structures. Their radiometric properties are embedded in a data term. A prior model includes interactions describing the clustering property of VRS. A Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC) optimizes the proposed model, obtained by multiplying the prior and the data model. Example results are shown on T/sub 1/-weighted MRI datasets of elderly subjects.