title: Application of the extremum stack to neurological MRI.
abstract: The extremum stack, as proposed by Koenderink, is a multiresolution image description and segmentation scheme which examines intensity extrema (minima and maxima) as they move and merge through a series of progressively isotropically diffused images known as scale space. Such a data-driven approach is attractive because it is claimed to be a generally applicable and natural method of image segmentation. The performance of the extremum stack is evaluated here using the case of neurological magnetic resonance imaging data as a specific example, and means of improving its performance proposed. It is confirmed experimentally that the extremum stack has the desirable property of being shift-, scale-, and rotation-invariant, and produces natural results for many compact regions of anatomy. It handles elongated objects poorly, however, and subsections of regions may merge prematurely before each region is represented as a single node. It is shown that this premature merging can often be avoided by the application of either a variable conductance-diffusing preprocessing step, or more effectively, the use of an adaptive variable conductance diffusion method within the extremum stack itself in place of the isotropic Gaussian diffusion proposed by Koenderink.
authors.affiliation: Department of Clinical Neurosciences, Institute of Psychiatry, London, UK. a.simmons@iop.bpmf.ac.uk
authors.forename: A
authors.initials: A
authors.lastname: Simmons
authors.forename: S R
authors.initials: SR
authors.lastname: Arridge
authors.forename: P S
authors.initials: PS
authors.lastname: Tofts
authors.forename: G J
authors.initials: GJ
authors.lastname: Barker
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*