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Performance Analysis of Various Feature Sets for Malaria-Infected Erythrocyte Detection

  • Salam Shuleenda DeviEmail author
  • Ngangbam Herojit Singh
  • Rabul Hussain Laskar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)

Abstract

Malaria being prevalent disease in urban areas, demands its accurate and fast diagnosis. Due to malaria infection in human being, the erythrocyte features got distorted. To diagnose these, various techniques have been developed, i.e., machine learning-based system, rapid diagnostic test, quantitative buffy coat, etc. In machine learning, the system performance depends on the feature set and classifier model. In this paper, the analysis of the importance of the feature set on malaria-infected erythrocyte classification has been performed. Further, a classifier model based on ANN-GA has been developed to classify the erythrocyte. The process consists of illumination correction, erythrocyte segmentation, feature extraction with or without feature selection techniques, and classification. Erythrocytes segmentation is done using image binarization with marker-controlled watershed segmentation. The six feature sets (morphological feature, texture and intensity feature) have been evaluated using various classifiers such as support vector machine (SVM), k-nearest neighbor (k-NN), and Naive Bayes to choose the better feature set. From the experimental results, it has been observed that the feature set \(\textit{f}_6\) (combination of morphological, texture and intensity feature ranked with ANOVA) outperforms other feature sets. Further, erythrocyte classification has been performed using ANN-GA with \(\textit{f}_6\) feature set. It may also conclude that the various features such as morphological feature, texture and intensity feature are equally important to detect the malaria-infected erythrocyte.

Keywords

Malaria Erythrocyte Intensity and texture feature Morphological feature Feature selection 

Notes

Acknowledgements

The research work has been done in the Speech and Image Processing Laboratory of NIT Silchar, Assam-788010. For malaria parasite identification and database collection, we would like to express our gratitude to Dr. S. A. Sheikh, Silchar Medical College and Hospital, Assam and Dr. A. Talukdar, Head of the Department, Pathology, Cachar Cancer Hospital and Research Centre, Assam.

References

  1. 1.
    Cuomo, M.J., Noel, L.B., White, D.B.: Diagnosing Medical Parasites: A Public Health Officers Guide to Assisting Laboratory and Medical Officers http://www.phsource.us/PH/PARA/ Diagnosing Medical Parasites (2012)
  2. 2.
    Di, Ruberto C., Dempster, A., Khan, S., Jarra, B.: Analysis of infected blood cell images using morphological operators. Image Vis. Comput. 20(2), 133–146 (2002)CrossRefGoogle Scholar
  3. 3.
    Nicholas, R.E., Charles, J.P., David, M.R., Adriano, G.D.: Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med Biol Eng Comput. 44(5), 427–436 (2006)CrossRefGoogle Scholar
  4. 4.
    Tek, F.B., Dempster, A.G., Kale, I.: Parasite detection and identification for automated thin blood film malaria diagnosis. Comput Vis Image Und. 114(1), 21–32 (2010)CrossRefGoogle Scholar
  5. 5.
    Diaz, G., Gonzalez, F.A., Romero, E.: A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. J. Biomed. Inform. 42(2), 296–307 (2009)CrossRefGoogle Scholar
  6. 6.
    Springl, V.: Automatic Malaria Diagnosis Through Microscopic Imaging. Faculty of Electrical Engineering, Prague (2009)Google Scholar
  7. 7.
    Das, D.K., Ghosh, M., Pal, M., Maiti, A.K., Chakraborty, C.: Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron 45, 97–106 (2013)CrossRefGoogle Scholar
  8. 8.
    Devi, S.S., Sheikh, S.A., Laskar, R.H.: Erythrocyte features for malaria parasite detection in microscopic images of thin blood smear: a review. Int. J. Interact. Multimed Artif. Intel. 4(2), 35–39 (2016)Google Scholar
  9. 9.
    Devi, S.S., Kumar, R., Laskar, R.H.: Recent advances on erythrocyte image segmentation for biomedical applications. In: Fourth International Conference on Soft Computing for Problem Solving (pp. 353–359). Springer, India (2015)Google Scholar
  10. 10.
    Devi, S.S., Roy, A., Singha, J., Sheikh, S.A., Laskar, R.H.: Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear. Multimedia Tools Appl. (2016).  https://doi.org/10.1007/s11042-016-4264-7CrossRefGoogle Scholar
  11. 11.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys. Man and Cyber 9(1), 62–66 (1979)CrossRefGoogle Scholar
  12. 12.
    Devi, S.S., Singha, J., Sharma, M., Laskar, R.H.: Erythrocyte segmentation for quantification in microscopic images of thin blood smears. J. Intell. Fuzzy Syst. 32(4), 2847–2856 (2017)CrossRefGoogle Scholar
  13. 13.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)CrossRefGoogle Scholar
  14. 14.
    Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn Res. 10, 207–244 (2009)zbMATHGoogle Scholar
  15. 15.
    Russell S, Norvig P (2003) Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall. ISBN 978-0137903955Google Scholar
  16. 16.
    Ahmad, F., Mat-Isa, N.A., Hussain, Z., Boudville, R., Osman, M.K.: Genetic algorithm-artificial neural network (GA-ANN) hybrid intelligence for cancer diagnosis. In: 2nd International Conference on Computational Intelligence, Communication Systems and Networks, pp. 78–83 (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Salam Shuleenda Devi
    • 1
    Email author
  • Ngangbam Herojit Singh
    • 1
  • Rabul Hussain Laskar
    • 2
  1. 1.National Institute of Technology MizoramAizawlIndia
  2. 2.National Institute of Technology SilcharSilcharIndia

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