Automated image processing method for the diagnosis and classification of malaria on thin blood smears

Medical and Biological Engineering and Computing

Volumn 44, Issue 5, 2006, Pages 427-436

  Ross, Nicholas E ^a   Pritchard, Charles J ^b   Rubin, David M ^b   Dusé, Adriano G ^c  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b UNIVERSITY OF THE WITWATERSRAND   (South Africa)

^c UNIVERSITY OF THE WITWATERSRAND MEDICAL SCHOOL   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; BLOOD; CELLS; DIAGNOSIS; DISEASES; FEEDFORWARD NEURAL NETWORKS; IMAGE PROCESSING; MEDICAL IMAGING; MICROBIOLOGY;

AUTOMATED IMAGE PROCESSING; BLOOD CELLS; MALARIA; THIN BLOOD SMEARS;

MALARIA CONTROL;

ALGORITHM; ARTICLE; AUTOMATION; BLOOD SMEAR; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISEASE CONTROL; ERYTHROCYTE; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; HUMAN; HUMAN CELL; IMAGE ANALYSIS; IMAGE PROCESSING; LABORATORY DIAGNOSIS; MALARIA; MICROSCOPY; NERVE CELL NETWORK; PARASITE IDENTIFICATION; PLASMODIUM FALCIPARUM; PLASMODIUM MALARIAE; PLASMODIUM OVALE; PLASMODIUM VIVAX; PREDICTION; SAMPLING; SENSITIVITY ANALYSIS;

ALGORITHMS; ANIMALS; AUTOMATIC DATA PROCESSING; ERYTHROCYTES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MALARIA; PLASMODIUM FALCIPARUM; SENSITIVITY AND SPECIFICITY; SPECIMEN HANDLING;

EID: 33745478313     PISSN: 01400118     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11517-006-0044-2     Document Type: Article

References (18)

1. Bloland PB (2001) Drug resistance in malaria, WHO/CDS/CSR/DRS/ 2001.4. World Health Organization, Switzerland
2. Bruce-Chwatt LJ (1985) Essential malariology, 2nd edn. William Heinemann Medical Books, London
3. Di Ruberto C, Dempster A, Khan S, Jarra B (2002) Analysis of infected blood cell images using morphological operators. Image Vis Comput 20(2):133-146
4. Foley DH (1972) Consideration of sample and feature size. IEEE Trans Inform Theory IT-18 618-626
5. Foster S, Phillips M (1998) Economics and its contribution to the fight against malaria. Ann Trop Med Parasitol 92:391-398
6. Hibbard LS, McCasland JS, Brunstrom JE, Pearlman AL (1996) Automated recognition and mapping of immmunolabelled neurons in the developing brain. J Microsc 183(8):241-256
7. Hughes GF (1968) On the mean accuracy of statistical pattern recognition. IEEE Trans Inform Theory IT-14, 55-63
8. Makler MT, Palmer CJ, Alger AL (1998) A review of practical techniques for the diagnosis of malaria. Ann Trop Med Parasitol 92(4):419-433
9. Mohana Rao KNR, Dempster AG (2001) Area-granulometry: An improved estimator of size distribution of image objects. Electron Lett 37(15):950-951
10. Mui JK, Fu K-S (1980) Automated classification of nucleated blood cells using a binary tree classifier. IEEE Trans Pattern Anal Machine Intell 2(5):429-443
11. Oaks SCJ, Mitchell VS, Pearson GW, Carpenter CCJ (eds) (1991) Malaria: obstacles and opportunities, National Academy Press, Washington, DC. A report of the committee for the study on malaria prevention and control: Status review and alternative strategies
12. Ohrt C, Purnomo, Sutamihardja MA, Tang D, Kain KC (2002) Impact of microscopy error on estimates of protective efficacy in malaria-prevention trials. J Infect Dis 186(4):540-546
13. Otsu N (1979) A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern SMC-9(1):62-66
14. Pammenter MD (1988) Techniques for the diagnosis of malaria. S Afr Med J 74(2):55-57
15. Soni PN, Gouws E (1996) Severe and complicated malaria in KwaZulu-Natal. S Afr Med J 86(6):653-656
16. Theodoridis S, Koutroumbas K (1999) Pattern Recognition. Academic, San Diego
17. Thiran J-P, Macq B (1996) Morphological feature extraction for the classification of digitalim ages of cancerous tissues. IEEE Trans Biomed Eng 43(10):1011-1019
18. World Health Organisation (1991) Basic malaria microscopy. World Health Organisation, Geneva