Machine learning approach for automated screening of malaria parasite using light microscopic images

Micron

Volumn 45, Issue , 2013, Pages 97-106

  Das, Dev Kumar ^a   Ghosh, Madhumala ^a   Pal, Mallika ^b   Maiti, Asok K ^b   Chakraborty, Chandan ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

^b Midnapore Medical College and Hospital   (India)

Author keywords

Bayesian classifier; Erythrocyte; Machine learning; Malaria parasite; Texture

Indexed keywords

AUTOMATED SCREENING; BAYESIAN APPROACHES; BAYESIAN CLASSIFIER; BAYESIAN LEARNING; CLASSIFICATION ACCURACY; COMPUTER ASSISTED; ERYTHROCYTE; ILLUMINATION CORRECTION; LEARNING APPROACH; MALARIA PARASITE; MICROSCOPIC IMAGE; PARASITEMIA; PERIPHERAL BLOOD SMEARS; PLASMODIUM FALCIPARUM; PLASMODIUM VIVAX; SELECTION FRAMEWORK; STATISTICAL LEARNING TECHNIQUES; WATERSHED TRANSFORMATIONS;

AUTOMATION; BAYESIAN NETWORKS; DISEASES; FEATURE EXTRACTION; LEARNING SYSTEMS; NOISE ABATEMENT; SUPPORT VECTOR MACHINES; TEXTURES;

BLOOD;

ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATION; BLOOD; DIAGNOSTIC PROCEDURE; EVALUATION; HUMAN; MALARIA FALCIPARUM; MASS SCREENING; METHODOLOGY; MICROSCOPY; PARASITEMIA; PARASITOLOGY; PLASMODIUM VIVAX MALARIA;

ARTIFICIAL INTELLIGENCE; AUTOMATION; BLOOD; CLINICAL LABORATORY TECHNIQUES; HUMANS; MALARIA, FALCIPARUM; MALARIA, VIVAX; MASS SCREENING; MICROSCOPY; PARASITEMIA; PARASITOLOGY;

PLASMODIUM FALCIPARUM; PLASMODIUM VIVAX;

EID: 84872497956     PISSN: 09684328     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.micron.2012.11.002     Document Type: Article

References (41)

1. Beucher S., Meyer F. The morphological approach to segmentation: the watershed transformation. Mathematical Morphology in Image Processing 1993, vol. 34. Marcel Dekker, New York, pp. 433-481 (Chapter 12).
2. Chu A., Schgal C.M., Greenleaf J.F. Use of gray value distribution of run lengths for texture analysis. Pattern Recognition Letter 1990, 11(6):415-420.
3. Cuomo, M.J., Noel, L.B., White, D.B., 2012. Diagnosing Medical Parasites: A Public Health Officers Guide to Assisting Laboratory and Medical Officers. Retrieved from http://www.phsource.us/PH/PARA/Diagnosing_Medical_Parasites.pdf.
4. Das D., Ghosh M., Chakraborty C., Pal M., Maity A.K. Invariant moment based feature analysis for abnormal erythrocyte recognition. Proceedings of IEEE, ICSMB, IEEE 2010, 242-247.
5. Dasarathy B.R., Holder E.B. Image characterization based on joint gray-level run-length distribution. Pattern Recognition Letter 1991, 12(8):497-502.
6. Dempster A., Ruberto C.D. Morphological processing of malarial slide images. Matlab DSP Conference 1999, 16-17.
7. Diaz G., Gonzalez F.A., Romero E. A semi automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic image. Journal of Biomedical Informatics 2009, 42(2):296-307.
8. Duda R., Hart P.E., Stork D.G. Pattern Classification 2007, Wieley Pub, New Delhi. 2nd ed.
9. Frean J. Microscopic determination of malaria parasite load: role of image analysis. Microscopy: Science, Technology, Application and Education, FORMATEX 2010, 3:862-866.
10. Galloway M.M. Texture analysis using gray level run lengths. Computer Graphics and Image Processing 1975, 4(2):172-179.
11. Gonzalez R.C., Woods R.E. Digital Image Processing 2002, Prentice Hall, New York. 2nd ed.
12. Ghosh M., Das D., Chakraborty C. Entropy based divergence for leukocyte image segmentation. Proceedings of IEEE, ICSMB, IEEE 2010, 409-413.
13. Greer J.P., Foerster J., Rodgers G.M., Paraskevas F., Glader B., et al. Wintrobe's Clinical Hematology 2009, Lippincott Williams & Wilkins, Philadelphia. 12th ed.
14. Gun A.M., Gupta M.K., Dasgupta B. Fundamentals of Statistics 2008, vol. 2. The World Press Pvt. Ltd., Kolkata, India.
15. Han J., Kamber M. Data Mining: Concept and Techniques 2006, Morgan Kaufmann Publishers, San Francisco, USA.
16. Haralick R.M., Shanmugam K., Dinstein I. Textural features for image classification. IEEE Transaction on Systems. Man and Cybernetics 1973, 3(6):610-621.
17. Hue M.K. Visual pattern recognition by moment invariants. IRE Transaction on Information Theory 1962, 8(2):179-187.
18. Krishnan M.M.R., Shah P., Choudhury A., Chakraborty C., Paul R.R., et al. Textural characterization of histopathological images for oral sub-mucous fibrosis detection. Tissue and Cell 2011, 43(5):318-330.
19. Krishnan M.M.R, Shah P., Chakraborty C., Ray A.K. Statistical analysis of textural features for improved classification of oral histopathological images. Journal of Medical Systems 2012, 36(2):865-881.
20. Kumar S., Ong S.H., Raganath S., Ong T.C., Chew F.T. A rule-based approach for robust clump splitting. Pattern Recognition Letter 2006, 39(6):1088-1098.
21. Lam E.Y. Combining gray world and retinex theory for automatic white balance in digital photography. Proceedings of International Symposium on Consumer Electronics 2005, 134-139.
22. Makkapati V.V., Rao R.M. Segmentation on malaria parasites in peripheral blood smear images. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2009, 1361-1364.
23. Mandelbrot B.B. Fractal Geometry of Nature 1982, W. H. Freeman and Company, New York.
24. Martis R.J., Acharya R.U., Mandana K.M., Ray A.K., Chakraborty C. Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Systems with Applications 2012, 39(14):11792-11800.
25. National vector borne disease control programme. Trend of malaria (2010-2011). Available at http://nvbdcp.gov.in/malaria9.html.
26. Ojala T., Pietikainen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(7):971-981.
27. Pharwaha A.P.S., Sing B. Shannon and non-Shannon measures of entropy for statistical texture feature extraction in digitized mammograms. Proceedings of WCECS, vol. I/II 2009, 1286-1291.
28. Rastogi V. Fundamentals of Biostatistics 2008, Ane Books, India.
29. Raviraja S., Osman S.S., Kardman A novel technique for malaria diagnosis using invariant moments and by image compression. IFMBE Proceedings of 4th International Conference on Biomedical Engineering, vol. 21 2008, 730-733.
30. Raviraja S., Bajpai G., Sharma S.K. Analysis of detecting the malarial parasite infected blood images using statistical based approach. IFMBE Proceedings of 3rd International conference on Biomedical Engineering, vol. 15 2006, 534-537.
31. Ross N.E., Pritchard C.J., Rubin D.M., Duse A.G. Automatic image processing method for the diagnosis and classification of malaria on thin blood smears. Medical and Biological Engineering Computing 2006, 44(5):427-436.
32. Sarkar N., Choudhury B.B. An efficient differential box-counting approach to compute fractal dimension of image. IEEE Transaction on Systems, Man and Cybernetics 1994, 24(1):115-120.
33. Sio S.W.S., Sun W., Kumar S., Bin W.Z., Tan S.S., et al. Malaria count: an image analysis-based program for the accurate determination of parasitemia. Journal of Microbiological Methods 2007, 68(1):11-18.
34. Tan J.H., Ng E.Y.K., Acharya R.U., Chee C. Study of normalocular thermogram using textural parameters. Infrared Physics & Technology 2010, 53(2):120-126.
35. Tan J.H., Ng E.Y.K., Acharya U.R., Chee C. Infrared thermography on ocular surface temperature: a review. Infrared Physics & Technology 2009, 52(4):97-108.
36. Tang X. Texture information in run-lengths matrices. IEEE Transaction on Image Processing 1998, 7(11):1602-1609.
37. Tangpukdee N., Duangdee C., Wilairatana P., Krudsood S. Malaria diagnosis: a brief review. Korean Journal of Parasitology 2009, 47(2):93-102.
38. Tek F.B., Dempster A.G., Kale I. Malaria parasite detection in peripheral blood images. Proceeding of British Machine Vision Conference 2006.
39. Thangavel K., Manavalan R., Laurence Aroquiaraj I. Removal of speckle noise from ultrasound medical image based on special filters: comparative study. ICGST-GVIP Journal 2009, 9(3):25-32.
40. Toha S.F., Ngah U.K. Computer Aided Medical Diagnosis for the Identification of Malaria Parasites. Proceedings of IEEE ICSCN 2007, 521-522.
41. WHO Basic Malaria Microscopy: Part I. Learner's Guide 2010, World Health Organization, Geneva, Switzerland, pp. 51-67. 2nd ed.