Automated Colour Segmentation of Malaria Parasite with Fuzzy and Fractal Methods

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)


Malaria is an endemic, global and life threatening disease. Technically skilled person or an expert is needed to analyze the microscopic blood smears for long hours. This paper presents an efficient approach to segment the parasites of malaria. Three different colour space namely LAB, HSI and gray has been used effectively to pre-process and segment the parasite in digital images with noise, debris and stain. L and B plane of LAB, S plane of HSI of input image is extracted with convolution and DCT. Fuzzy based segmentation has been proposed to segment the malaria parasite. Colour features, fractal features are extracted and feature vectors are prepared as a result of segmentation. Adaptive Resonance Theory Neural Network (ARTNN), Back Propagation Network (BPN) and SVM classifiers are used with Fuzzy segmentation and fractal feature extraction methods. Automated segmentation with ARTNN has recorded an accuracy of 95 % compared to other classifiers.


Fuzzy segmentation Fractal features ARTNN BPN SVM 


  1. 1.
    Makkapati, V.V., Rao, R.M.: Segmentation of malaria parasites in peripheral blood smear images. IEEE. 978-1-4244-2354-5/09 (2009)Google Scholar
  2. 2.
    Seman, N.A., Isa, N.A.M., Li, L.C., Mohamed, Z., Ngah, U.K., Zamli, K.Z.: Classification of malaria parasite species based on thin blood smears using multilayer perceptron network. Int. J. Comput. Internet Manag. 16, 46–52 (2008) Google Scholar
  3. 3.
    Hirimutugoda, M.Y.M., Wijayarathna, G.: Image analysis system for detection of red cell disorders using artificial neural networks. J. Bio-Med. Inf. 1, 35–42 (2010)Google Scholar
  4. 4.
    Savkare, S.S., Narote, S.P.: Automatic detection of malaria parasites for estimating parasitemia. Int. J. Comput. Sci. Secur. (IJCSS) 5, 310 (2011)Google Scholar
  5. 5.
    Boray Tek, F., Dempster, A.G., Kale, I.: Malaria parasite detection in peripheral blood Images. In: Proceedings of Medical Imaging. Annual Conference, Manchester, UK (2006)Google Scholar
  6. 6.
    Karmakar, Gour C., Dooley, Laurence S.: A generic fuzzy rule based image segmentation algorithm. Pattern Recogn. Lett. 23, 1215–1227 (2002)CrossRefMATHGoogle Scholar
  7. 7.
    Wertheimer, M.: Laws of organization in perceptual forms. Pshychol. Forsch. 6 (1923)Google Scholar
  8. 8.
    Kellogg, C.B., Zhao, F., Yip, K.: Spatial aggregation: language and applications. In: International Proceedings of AAAI (1996)Google Scholar
  9. 9.
    Yip, K., Zhao, F.: Spatial aggregation: theory and applications. J. Artif. Intell. Res. 5, 1–26 (1996)Google Scholar
  10. 10.
    Alamdar, F., Keyvanpour, M.R.: A new colour feature extraction method based on dynamic colour distribution entropy of neighborhoods. IJCSI Int. J. Comput. Sci. Issues. 8, 42 (2011)Google Scholar
  11. 11.
    Tang, Y.Y., Tao, Y., Ernest, C.M.: Lam: new method for feature extraction based on fractal behavior. Pattern Recogn. 35, 1071–1081 (2002)Google Scholar
  12. 12.
    Dobrescu, R., Ionescu, F.: Fractal dimension based technique for database image retrieval. In: IONESCU (2003)Google Scholar
  13. 13.
    Jain, A.K., Mao, J., Mohiuddin, K.M.: IEEE (1996)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.JSSATEBangaloreIndia
  2. 2.RNSITBangaloreIndia

Personalised recommendations