Journal article
Neuron recognition by parallel Potts segmentation
Proceedings of the National Academy of Sciences - PNAS, v 100(7), pp 3847-3852
01 Apr 2003
PMID: 12651959
Abstract
Identifying neurons and their spatial coordinates in images of the cerebral cortex is a necessary step in the quantitative analysis of spatial organization in the brain. This is especially important in the study of Alzheimer's disease (AD), in which spatial neuronal organization and relationships are highly disrupted because of neuronal loss. To automate neuron recognition by using high-resolution confocal microscope images from human brain tissue, we propose a recognition method based on statistical physics that consists of image preprocessing, parallel image segmentation, and cluster selection on the basis of shape, optical density, and size. We segment a preprocessed digital image into clusters by applying Monte Carlo simulations of a
q
-state inhomogeneous Potts model. We then select the range of Potts segmentation parameters to yield an ideal recognition of simplified objects in the test image. We apply our parallel segmentation method to control individuals and to AD patients and achieve recognition of 98% (for a control) and 93% (for an AD patient), with at most 3% false clusters.
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Details
- Title
- Neuron recognition by parallel Potts segmentation
- Creators
- S Peng - Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215; andB Urbanc - Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215; andL Cruz - Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215; andB. T Hyman - Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215; andH. E Stanley - Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215; and
- Publication Details
- Proceedings of the National Academy of Sciences - PNAS, v 100(7), pp 3847-3852
- Publisher
- National Academy of Sciences
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Physics
- Web of Science ID
- WOS:000182058400058
- Scopus ID
- 2-s2.0-0037389773
- Other Identifier
- 991014878018204721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Neurosciences