Paper Info
Title
Neuron geometry extraction by perceptual grouping in ssTEM images
Abstract
In the field of neuroanatomy, automatic segmentation of electron microscopy images is becoming one of the main limiting factors in getting new insights into the functional structure of the brain. We propose a novel framework for the segmentation of thin elongated structures like membranes in a neuroanatomy setting. The probability output of a random forest classifier is used in a regular cost function, which enforces gap completion via perceptual grouping constraints. The global solution is efficiently found by graph cut optimization. We demonstrate substantial qualitative and quantitative improvement over state-of the art segmentations on two considerably different stacks of ssTEM images as well as in segmentations of streets in satellite imagery. We demonstrate that the superior performance of our method yields fully automatic 3D reconstructions of dendrites from ssTEM data.
Year
DOI
Venue
2010
10.1109/CVPR.2010.5540029
Computer Vision and Pattern Recognition
Keywords
Field
DocType
feature extraction,geometry,graph theory,image classification,image reconstruction,image segmentation,medical image processing,optimisation,probability,transmission electron microscopy,automatic 3D reconstructions,cost function,dendrites,electron microscopy image segmentation,graph cut optimization,membranes,neuroanatomy,neuron geometry extraction,perceptual grouping constraint,probability output,random forest classifier,satellite imagery,ssTEM images,street segmentations,thin elongated structure segmentation
Cut,Iterative reconstruction,Computer vision,Pattern recognition,Computer science,Segmentation,Image segmentation,Feature extraction,Artificial intelligence,Pixel,Contextual image classification,Random forest
Conference
Volume
Issue
ISSN
2010
1
1063-6919
ISBN
Citations 
PageRank 
978-1-4244-6984-0
36
3.19
References 
Authors
15
3
Name
Order
Citations
PageRank
Verena Kaynig1788.86
Thomas J. Fuchs234322.48
joachim m buhmann34363730.34