Region-Based Segmentation of Parasites for High-throughput Screening

  • Asher Moody-Davis
  • Laurent Mennillo
  • Rahul Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

This paper proposes a novel method for segmenting microscope images of schisotsomiasis. Schistosomiasis is a parasitic disease with a global impact second only to malaria. Automated analysis of the parasite’s reaction to drug therapy enables high-throughput drug discovery. These reactions take the form of phenotypic changes that are currently evaluated manually via a researcher viewing the video and assigning phenotypes. The proposed method is capable of handling the unique challenges of this task including the complex set of morphological, appearance-based, motion-based, and behavioral changes of parasites caused by putative drug therapy. This approach adapts a region-based segmentation algorithm designed to quickly identify the background of an image. This modified implementation along with morphological post-processing provides accurate and efficient segmentation results. The results of this algorithm improve the correctness of automated phenotyping and provide promise for high-throughput drug screening.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Asher Moody-Davis
    • 1
  • Laurent Mennillo
    • 2
  • Rahul Singh
    • 1
  1. 1.Department of Computer ScienceSan Francisco State UniversitySan FranciscoUSA
  2. 2.Universite De La Mediterranee Aux-Marseille IIMarseilleFrance

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