Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy

Author:

Poostchi Mahdieh1,Ersoy Ilker2,McMenamin Katie3,Gordon Emile4,Palaniappan Nila5,Pierce Susan4,Maude Richard J.6,Bansal Abhisheka7,Srinivasan Prakash8,Miller Louis8,Palaniappan Kannappan9,Thoma George1,Jaeger Stefan1

Affiliation:

1. Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, M

2. University of Missouri-Columbia, Informatics Institute, Missouri

3. University of Colorado Boulder, Aerospace Engineering Sciences Department, Boulder, Colorado

4. National Institute of Allergy and Infectious Diseases, Division of Intramural Research, Rockville, M

5. University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri

6. University of Oxford, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicin

7. Jawaharlal Nehru University, School of Life Sciences, New Delhi

8. National Institute of Allergy and Infectious Diseases, Laboratory of Malaria and Vector Research, Ro

9. University of Missouri-Columbia, Department of Electrical Engineering and Computer Science, Columbia

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology Nuclear Medicine and imaging

Reference56 articles.

1. Image analysis and machine learning for detecting malaria

2. Microscopic determination of malaria parasite load: role of image analysis;Frean,2010

3. Automated blood smear analysis for mobile malaria diagnosis;Quinn,2014

4. Malaria Diagnosis Using a Mobile Phone Polarized Microscope

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