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)


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.


Canny Edge Detection Merge Region Morphological Segmentation Canny Edge Detection Algorithm Relevant Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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