An adjustable-threshold algorithm for the identification of objects in three-dimensional images.
BACKGROUND
To develop a highly accurate, practical and fast automated segmentation algorithm for three-dimensional images containing biological objects. To test the algorithm on images of the Drosophila brain, and identify, count and determine the locations of neurons in the images.


RESULTS
A new adjustable-threshold algorithm was developed to efficiently segment fluorescently labeled objects contained within three-dimensional images obtained from laser scanning confocal microscopy, or two-photon microscopy. The result of the test segmentation with Drosophila brain images showed that the algorithm is extremely accurate and provided detailed information about the locations of neurons in the Drosophila brain. Centroids of each object (nucleus of each neuron) were also recorded into an algebraic matrix that describes the locations of the neurons.


BACKGROUND
Interested parties should send their request for the NeuronMapper(TM) program with the segmentation algorithm to artemp@bcm.tmc.edu.
Ponomarev
Artem L
AL
Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA. artemp@bcm.tcm.edu
Davis
Ronald L
RL
D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods*; D000818:Animals; D001921:Brain / Q000166:cytology; D002467:Cell Nucleus / Q000648:ultrastructure*; D004056:Differential Threshold; D004331:Drosophila melanogaster; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D036641:Microscopy, Fluorescence, Multiphoton / Q000379:methods; D009474:Neurons / Q000166:cytology*; D010363:Pattern Recognition, Automated