Analysis of brain activation patterns using a 3-D scale-space primal sketch

• Published in: Human brain mapping Vol. 7; no. 3; pp. 166 - 194
• Main Authors: Lindeberg, Tony, Lidberg, Pär, Roland, Per E.
• Format: Journal Article
• Language: English
• Published: New York John Wiley & Sons, Inc 1999; Wiley-Liss
• Subjects: Biological and medical sciences; blob detection; brain activation; Brain Mapping - methods; computer vision; Electrodiagnosis. Electric activity recording; functional region; human brain mapping; Image Processing, Computer-Assisted; Investigative techniques, diagnostic techniques (general aspects); Mathematics; Medical sciences; Miscellaneous. Technology; multi-scale representation; multiscale representation; neuroimaging; Normal Distribution; primal sketch; scale selection; scale-space; Signal Processing, Computer-Assisted; Tomography, Emission-Computed; Human; Cartography; Cartographic representation; Three dimensional representation; Central nervous system; Three dimensional system; Activation; Technique; Automatic analysis; Anatomy; Spatial representation; Brain (vertebrata)
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/(SICI)1097-0193(1999)7:3<166::AID-HBM3>3.0.CO;2-I

A fundamental problem in brain imaging concerns how to define functional areas consisting of neurons that are activated together as populations. We propose that this issue can be ideally addressed by a computer vision tool referred to as the scale‐space primal sketch. This concept has the attractive properties that it allows for automatic and simultaneous extraction of the spatial extent and the significance of regions with locally high activity. In addition, a hierarchical nested tree structure of activated regions and subregions is obtained. The subject in this article is to show how the scale‐space primal sketch can be used for automatic determination of the spatial extent and the significance of rCBF changes. Experiments show the result of applying this approach to functional PET data, including a preliminary comparison with two more traditional clustering techniques. Compared to previous approaches, the method overcomes the limitations of performing the analysis at a single scale or assuming specific models of the data. Hum. Brain Mapping 7:166–194. © 1999 Wiley‐Liss, Inc.