February 1, 2017 Thesis Open Access

Abebe Bekele

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{
  "description": "<p>Addis Ababa University, 2017<br>\nMalaria is a serious global health problem and its diagnosis is usually done manually by compound light microscopy which is time consuming, tiresome and subjective. To support this manual method, in this master thesis, we designed and developed a system which is able to automatically detect plasmodium parasites from images of blood smears acquired by ourselves using a digital light microscope.<br>\nIn this method, blood smears taken from patients who were infected with plasmodium parasites were prepared. Digital images were then acquired by the light microscope and saved in the computer. Red blood cells (RBCs) are first segmented by marker control watershed algorithm, where the foreground markers are obtained from circular Hough transform and background markers from distance transform. The plasmodium infected RBCs are then detected in the Hue-Saturation-Intensity (HSI) color space. Thresholding on hue component of HSI color space is used to detect the chromatin dots of the parasite. Plasmodium falciparum and plasmodium vivax, the two dominant plasmodium species which cause the vast deaths in Ethiopia, are differentiated based on the size of infected RBCs.</p>", 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "creator": [
    {
      "@type": "Person", 
      "name": "Abebe Bekele"
    }
  ], 
  "headline": "Automatic Detection of Malaria Parasite based on Microscopic Image Analysis", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2017-02-01", 
  "url": "https://nadre.ethernet.edu.et/record/4870", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.20372/nadre:4870", 
  "@id": "https://doi.org/10.20372/nadre:4870", 
  "@type": "ScholarlyArticle", 
  "name": "Automatic Detection of Malaria Parasite based on Microscopic Image Analysis"
}