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

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.

[1]  A. Baddeley,et al.  Stochastic geometry models in high-level vision , 1993 .

[2]  Merrilee Hurn.  Confocal fluorescence microscopy of leaf cells: an application of Bayesian image analysis , 2002 .

[3]  J A Fiez,et al.  Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability , 2000, Human brain mapping.

[4]  Frederik Barkhof,et al.  White Matter Changes on CT and MRI: An Overview of Visual Rating Scales , 1998, European Neurology.

[5]  N Fujita,et al.  MR appearance of Virchow-Robin spaces along lenticulostriate arteries: spin-echo and two-dimensional fast low-angle shot imaging. , 1994, AJNR. American journal of neuroradiology.

[6]  Sven Loncaric,et al.  Quantitative intracerebral brain hemorrhage analysis , 1999, Medical Imaging.

[7]  Guido Gerig,et al.  Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data , 2000, Medical Image Anal..

[8]  Adrian Baddeley,et al.  ICM for Object Recognition , 1992 .

[9]  Michael I. Miller,et al.  Implementation of jump-diffusion algorithms for understanding FLIR scenes , 1995, Defense, Security, and Sensing.

[10]  Frithjof Kruggel,et al.  Computational Cost of Non-Rigid Registration Algorithms Based on Fluid Dynamics , 2002, IEEE Trans. Medical Imaging.

[11]  Niels Jørgen Christensen,et al.  Volume sculpting using the level-set method , 2002, Proceedings SMI. Shape Modeling International 2002.

[12]  L. Heier,et al.  Large Virchow-Robin spaces: MR-clinical correlation. , 1989, AJNR. American journal of neuroradiology.

[13]  Supun Samarasekera,et al.  Multiple sclerosis lesion quantification using fuzzy-connectedness principles , 1997, IEEE Transactions on Medical Imaging.

[14]  F Kruggel,et al.  Hippocampal volume discriminates between normal cognition; questionable and mild dementia in the elderly , 2001, Neurobiology of Aging.

[15]  D. Louis Collins,et al.  Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images , 1995, IEEE Trans. Medical Imaging.

[16]  A Dromigny-Badin,et al.  A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain MRI. , 1998, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[17]  Håvard Rue,et al.  Bayesian object recognition with baddeley's delta loss , 1998, Advances in Applied Probability.

[18]  Michael Unser,et al.  Image interpolation and resampling , 2000 .

[19]  Josiane Zerubia,et al.  A RJMCMC Algorithm for Object Processes in Image Processing , 2001, Monte Carlo Methods Appl..

[20]  Josef Kittler,et al.  Region growing: a new approach , 1998, IEEE Trans. Image Process..

[21]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.

[22]  Gary E. Christensen,et al.  Deformable Shape Models for Anatomy , 1994 .

[23]  William I. Grosky,et al.  Segmentation and representation of lesions in MRI brain images , 1999, Medical Imaging.

[24]  Josiane Zerubia,et al.  Road extraction in remote sensed images using stochastic geometry framework , 2001 .

[25]  Frithjof Kruggel,et al.  Computational cost of nonrigid registration algorithms based on fluid dynamics [MRI time series application] , 2002, IEEE Transactions on Medical Imaging.

[26]  Frithjof Kruggel,et al.  Alignment of magnetic-resonance brain datasets with the stereotactical coordinate system , 1999, Medical Image Anal..

[27]  Frithjof Kruggel,et al.  Segmentation of large brain lesions , 2001, IEEE Transactions on Medical Imaging.

[28]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[29]  H. Bokura,et al.  Distinguishing silent lacunar infarction from enlarged Virchow-Robin spaces: a magnetic resonance imaging and pathological study , 1998, Journal of Neurology.