title: Neuron branch detection and description using random walk.
abstract: The morphological studies of neuron structures are of great interests for biologists. However, manually detecting dendrites structures is very labor intensive, therefore unfeasible in studies that involve a large number of images. In this paper, we propose an automated neuron detection and description method. The proposed method uses ratios of probability maps from random walk sessions to detect initial seed-points and minimal cost path integrals with Delaunay triangulations.
authors: Universite Rene Descartes Paris 5, 75006 Paris. caleb007@ip-korea.org
authors: Hee Chang
authors: HC
authors: Kim
authors: Auguste
authors: A
authors: Genovesio
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D009415:Nerve Net / Q000166:cytology*; D009434:Neural Pathways / Q000166:cytology*; D009474:Neurons / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity