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Malaria Infected Erythrocyte Classification Based on the Histogram Features using Microscopic Images of Thin Blood Smear

Affiliations

  • Department of Electronics and Communication Engineering, National Institute of Technology, Silchar, Assam –788010, India
  • Department of Pathology, Silchar Medical College and Hospital, Silchar, Assam – 788014, India
  • Cachar Cancer Hospital and Research Centre, Silchar, Assam – 788015, India

Abstract


Objectives: This paper aims to develop a system for malaria infected erythrocyte classification based on the histogram feature set. Method: The method consist of pre-processing, segmentation, feature extraction based on the histogram of different color channels, feature selection and malaria infected erythrocyte classification using Artificial Neural Networks (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Naive Bayes. Findings: The experimental analysis of all the classifiers with the different combinations of features has been carried out on clinical database. Based on the experimental results it may be concluded that ANN provides the higher classification rate in comparison to other classifiers which provides an overall accuracy of 96.32% and F-score of 85.32% respectively. Applications: The proposed system may be used for the automatic recognition of the malaria infected erythrocytes in the thin blood smears.

Keywords

Erythrocyte, Histogram Features, Malaria, Microscopic Image, Thin Blood Smears.

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References


  • World Malaria Report 2013 [Internet]. 2015 [cited 2015 Apr 15]. Available from: http://www.who.int/malaria/publications/world_malaria_report_2013/report/en/.
  • Dhiman S, Baruah I, Singh L. Military malaria in northeast region of India. Defence Science Journal. 2010 Mar; 60(2):213–18.
  • Edison M, Jeeva JB, Singh M. Digital analysis of changes by plasmodium vivax malaria in erythrocytes. Indian Journal of Experimental Biology. 2011 Jan; 49:11–5.
  • Cuomo MJ, Noel LB, White DB. Diagnosing medical parasites: a public health officer’s guide to assisting laboratory and medical officers [Internet]. 2015 [cited 2015 May 21]. Available from: http://www.phsource.us/PH/PARA/Diagnosing Medical Parasites.
  • Devi SS, Kumar R, Laskar RH. Recent advances on erythrocyte image segmentation for biomedical applications. Proceedings of Fourth International Conference on Soft Computing for Problem Solving, India; 2015. p. 353–9.
  • Kumar R, Devi SS, Talukdar FA. State of the art survey on image segmentation technique. Proceedings of 3rd International Conference on Computing, Communication and Sensor Network, India; 2014. p. 87–91.
  • Prakash O, Verma M, Sharma P, Kumar M, Kumari K, Singh A, Kumari H, Jit S, Gupta SK, Khanna M, Lal R. Polyphasic approach of bacterial classification — an overview of recent advances. Indian Journal of Microbiology. 2007 Jun; 47(2):98–108.
  • Devi SS, Sheikh SA, Laskar RH. Erythrocyte features for malaria parasite detection in microscopic images of thin blood smear: a review. International Journal of Interactive Multimedia and Artificial Intelligence. 2016 Dec; 4(2): 35–9.
  • Ruberto CD, Dempster A, Khan S, Jarra B. Analysis of infected blood cell images using morphological operators. Image and Vision Computing. 2002 Feb; 20(2):133–46.
  • Tek FB, Dempster AG, Kale I. Malaria parasite detection in peripheral blood images. Proceedings of the British Machine Vision Conference, UK; 2006. p. 347–56.
  • Nicholas RE, Charles JP, David MR, Adriano GD. Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Medical and Biological Engineering and Computing. May 2006; 44 (5):427–36.
  • Diaz G, Gonzalez FA, Romero E. A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. Journal of Biomedical Informatics. April 2009; 42(2):296–307.
  • Springl V. Automatic malaria diagnosis through microscopic imaging. [Faculty of Electrical Engineering thesis]. Prague; 2009.
  • Ghosh M, Das D, Chakraborty C, Ray AK. Quantitative characterization of Plasmodium vivax in infected erythrocytes: a textural approach. International Journal of Artificial Intelligence and Soft Computing. 2013; 3(3): 203–21.
  • Savkare S, Narote S. Automatic detection of malaria parasites for estimating parasitemia. International Journal of Computer Science and Security. 2011; 5(3):310–5.
  • Memeu DM. A rapid malaria diagnostic method based on automatic detection and classification of plasmodium parasites in stained thin blood smear images. Doctoral dissertation, University of Nairobi; 2014.
  • Annaldas S, Shirgan SS, Marathe VR. Automatic identification of malaria parasites using image processing. International Journal of Emerging Engineering Research and Technology. 2014 Jul; 2(4):107–12.
  • Das DK, Maiti AK, Chakraborty C. Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears. Journal of Microscopy. 2015; 257(3):238–52.
  • Das DK, Ghosh M, Pal M, Maiti AK, Chakraborty C. Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron. 2013 Feb; 45:97–106.
  • Das DK, Ghosh M, Chakraborty C, Maiti AK, Pal M. Probabilistic prediction of malaria using morphological and textural information. Proceedings of International Conference on Image Information Processing, India; 2011.
  • Das DK, Maiti AK, Chakraborty C. Textural pattern classification of microscopic images for malaria screening. Advances in Therapeutic Engineering. CRC Press; 2012. p. 419–46.
  • Bairagi VK, Charpe KC. Comparison of texture features used for classification of life stages of malaria parasite. International Journal of Biomedical Imaging. 2016 May 9; 2016. DOI : 10.1155/2016/7214156.
  • Tek FB, Dempster AG, Kale I. A colour normalization method for giemsa-stained blood cell images. Proceedings of the Signal Processing and Communications Applications, Turkey; 2006.
  • Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on System, Man and Cybernetics. 1979; 9(1):62–6.
  • Cheng J, Rajapakse JC. Segmentation of clustered nuclei with shape markers and marking function. IEEE Transactions on Biomedical Engineering. 2009; 56(3):741–8.
  • Jung C, Kim C. Segmenting clustered nuclei using h-minima transform- based marker extraction and contour parameterization. IEEE Transactions on Biomedical Engineering. 2010; 57(10):2600–4.
  • Hahnel M, Klunder D, Kraiss, Color KF. Texture features for person recognition. Proceedings of IEEE International Joint Conference on Neural Networks; 2004. p. 647–52.
  • Siggelkow S. Feature histograms for content-based image retrieval. Dissertation, Universitat Freiburg; 2002.
  • Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician. 1992; 46(3):175–85.
  • Weinberger KQ, Saul LK. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research. 2009; 10:207–44.
  • Devi SS, Roy A, Sharma M, Laskar RH. kNN classification based erythrocyte separation in microscopic images of thin blood smear. Proceedings of 2nd International Conference on Computational Intelligence and Networks, India; 2016. p. 69–72.
  • Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995; 20(3):273–97.
  • Duda RO, Hart PE, Stork DG. Pattern classification. New Delhi: John Wiley and Sons; 2001.
  • Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 1998; 2(2):121–67.
  • Kumar RD, Ganesh AB, Sasikala S. Speaker identification system using mixture model and support vector machines (gmm-svm) under noisy conditions. Indian Journal of Science and Technology. 2016 May; 9(19):1–6. DOI: 10.17485/ijst/2016/v9i19/93870.
  • Roy A, Singha J, Devi SS, Laskar RH. Impulse noise removal using SVM classification based fuzzy filter from gray scale images. Signal Processing. 2016 Nov; 128:262–73.
  • Hagan MT, Demuth HB, Beale MH, Jesus OD. Neural network design. Boston: PWS publishing company; 1996.
  • Rizwan JM, Krishnan PN, Karthikeyan R, Kumar SR. Multi layer perception type artificial neural network based traffic control. Indian Journal of Science and Technology. 2016 Feb; 9(5):1–6. DOI:10.17485/ijst/2016/v9i5/87267.
  • Mamatha P, Venkatram N. Watermarking using Lifting Wavelet Transform (LWT) and Artificial Neural Networks (ANN). Indian Journal of Science and Technology. 2016 May; 9(17):1–7. DOI: 10.17485/ijst/2016/v9i17/93088.
  • Kavita K, Navin R, Shaifali Madan A. Piecewise feature extraction and artificial neural networks: an approach towards curve reconstruction. Indian Journal of Science and Technology. 2016 Jul; 9(28):1–9. DOI:10.17485/ijst/2016/v9i28/84138.
  • Jasjit SS, Dutta M, Aggarwal N. Efficacy of Artificial neural network based decision support system for career counseling. Indian Journal of Science and Technology. 2016 Aug; 9(32):1–9. DOI: 10.17485/ijst/2016/v9i32/100738.
  • Singha J, Laskar RH. ANN-based hand gesture recognition using self co articulated set of features. IETE Journal of Research. 2015 Jul; 61(6):597–608.
  • Roy A, Devi SS, Laskar RH. Impulse noise removal from gray scale images based on ANN classification based Fuzzy filter. Proceedings of 2nd International Conference on Computational Intelligence and Networks, India; 2016. p. 97–101.
  • Russell S, Norvig P. Artificial intelligence: a modern approach. 2nd edn. Upper Saddle River: Prentice hall; 2003.

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