Shape analysis and classification of lgl-type and wild-type neurons

Jinane Mounsef, Lina Karam, Patricia Estes, Daniela Zarnescu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Among the tumor suppressors identified in Drosophila, lgl is one of a few whose proliferative phenotype is shown to be secondary to a loss of cell polarity. Studies of lgl mutant phenotypes are likely to contribute to our understanding of both tumorigenesis as well as neural development mechanisms. However, alterations of neuronal development as a result of key protein mutations are not easy to describe without quantifiable parameters. This work presents a fully automated imaging technique that involves skeletonization and histogram-based features such as kurtosis to find and quantify discriminative morphological phenotypes that can be used to automatically distinguish normal wild-type neurons from lgl mutant neurons.

Original languageEnglish (US)
Title of host publication2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
Pages362-365
Number of pages4
DOIs
StatePublished - 2010
Event2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 - Niagara Falls, NY, United States
Duration: Aug 2 2010Aug 4 2010

Publication series

Name2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010

Other

Other2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
Country/TerritoryUnited States
CityNiagara Falls, NY
Period8/2/108/4/10

Keywords

  • Feature descriptor
  • Global kurtosis-based training
  • Lethal giant larvae (lgl) mutant phenotype
  • Morphological skeletonization
  • Multi-level kurtosis-based training
  • No-reference-based classification
  • Reduced-reference-based classification
  • Reference-based classification
  • Tree graph structure

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Information Management

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