Computer-Aided Diagnosis of Malaria Parsite using Patern Recognition Methods

Authors

1 Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

3 -Department of Entomology, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

Abstract

Background and Objectives: In many cases of paraitic identification by visual inspection is difficult, time consuming and depends heavily on the experience of microscopists. Computer-aided diagnosis can make a significant help in saving the time, reducing workforces and the possible operator errors. The aim of this study was to assess the performance of four classifiers for detection of malaria parasite was investigated.
Subjects and Methods:A total of 400 images of malaria parasite-infected blood slides were used.Intially by masking the red blood cells, in order to match the stained extracted elements, only red blood cells were used for next stage of the study. Then, the color histogram, granulometry, texture, saturation level histogram, gradient and flat­ texture features were extracted. For discriminating parasitic images from non-parasitic images four classifiers have been used: K-Nearest Neighbors (KNN), Nearest Mean (NM), 1-Nearest Neighbors (1NN), and Fisher linear discriminator (Fisher).
Results: The best classification accuracy of 92.5%, which was achieved by KNN classifier. The accuracies of 1-NN, Fisher and NM classifiers were 90.25%, 85%, and 60.25%, respectively.
Conclusion:Considering the performance of the proposed method, it can be used in the development of software for detecting malaria parasite. Thus, it can offer a significant help to researchers, managers and major planners to control malaria.

Keywords

References

1-UNICEF. World Malaria Report:Technical Report: WMR and UNICEF. 2012.Unicef Health. Available from: http://www.unicef.org/health/index_malaria.html. Accessed Jul 15, 2013.
2-Gallup J, Sachs J. The economic burden of malaria. Journal of Tropical Medicine 2001; 64 (Suppl 1-2): 85-96.
3-Coatney G, Collins W, Warren M, Contacos P. The Primate Malarias . Washington: National Academy Press; 1971.
4-Mui JK, Fu KS. Automated classification of nucleated blood cells using a binary tree classifier. IEEE Trans Pattern Analysis and Machine Intelligence 1980; 2(5): 429-43.
5-Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Systems, Man & Cybernetics 1979; 9 (1): 62–6.
6-Korn TM, Korn GA, editors. Mathematical Handbook for Scientists and Engineers.New York: Dover Publications;  2000 .
 7-Rodenaker K, Bengtsson E. A feature set for cytometry on digitized microscopic images. Anal Cell  Pathol 2003;  25(2): 1-36.
8- Boray TF. "Computerised Diagnosis of Malaria". PhD Thesis. London: University of Westminster, 2007.
9-Ruberto CD, Dempster A, Khan S, Jarra B. Analysis of infected blood cell images using morphological operators. Image Vision Comput 2002; 20:133–46.
10-Yang PF, Maragos P. Morphological systems for character image processing and recognition. In:Proceedings of  IEEE Int Acoustics Conference; 1993 Sep 13-15; Minneapolis, USA. New York; 2002.
11-Rao KNRM,  Dempster A. Area-granulometry: an improved estimator of size distribution of image objects. IEE Electron Lett 2001; 347(4): 950-51.
12-Theodoridis S, Koutroumbas K. Pattern recognition and neural networks.  Machine Learning and Its Applications 2001; 12(2): 169-15.
13-Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG-based brain–computer interfaces. Journal of neural engineering 2005; 4(2): 12-5.
14-Shashua A. the  relationship between the support vector machine for classification and sparsified Fisher's linear discriminant. Neural Processing Letters 1999; 9(2): 129-39.
15-Tek FB, Dempster AG, Kale I. Parasite detection and identification for automated thin blood film malaria diagnosis. Computer Vision and Image Understanding 2010; 114: 21–32.
16-Ruberto  CD, Dempster A, Khan  S, Jarra B. Analysis of infected blood cell images using morphological operators. Image Vis. Comput 2002; 20)2): 133–46.
17-Ross NE, Pritchard CJ, Rubin  DM, Dusé  AG.  Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med Biol Eng Comput 2006; 44: 427–36.
18- SWS S, Sun W, Kumar S, Bin WZ, Tan SS. MalariaCount: An image analysis-based program for the accuratedetermination of parasitemia. Journal of Microbiological Methods 2006; 2)2):13–6.
19-Diaz G, Gonzalez F, Romero E. Infected Cell Identification in thin Blood Images Based on Color Pixel Classification: Comparison and Analysis. J Biomed Inform 2007; 4756: 812-21.
20-Kumarasamy SK, Ong SH, Tan KSW. Robust contour reconstruction of red blood cells and   parasites in the automated identification of the stages of malarial infection. Machine Vision and Applications springer 2011; 22(1): 461-9.
21-Das D, Ghosh M, Chakraborty C.  Probabilistic   Prediction of Malaria using Morphological and  Textural Information. India, Bengal:  International Conference on Image Information Processing (ICIIP);  2011.
 
 
 
Jundishapur Scientific Medical Journal
Volume 14, Issue 1 - Serial Number 94
May and June 2015
Pages 66-74

Files

History

  • Receive Date: 29 September 2004
  • Revise Date: 13 October 2004
  • Accept Date: 30 December 2014

Share

How to cite

Statistics