Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans
Fig 7
Second layer classifier for cell pattern recognition and identification.
a) Representative maximum intensity projection and schematic representation of the two neuron pairs in which an insulin-like peptide is expressed. b) The modularity of our scheme permits the preprocessing and layer 1 classification components from neuron pair detection to be re-used for the recognition and identification of these neuron pairs. c) To identify the pattern with the appropriate cell identifications, properties for all possible combinations and arrangements of the layer 1 candidates are calculated. Here, all six such candidate sets for 4 candidate particles are shown. d) Validation of the SVM classifier trained with these features shows high specificity but only moderate sensitivity. e) The lower sensitivity observed for this classification scheme is mainly due to the limit ability to accommodate biological deviations from the stereotypical arrangement of the neurons while still maintaining high specificity.