Hybrid Segmentation of Malaria-Infected Cells in Thin Blood Slide Images

  • Sayantan Bhattacharya
  • Anupama BhanEmail author
  • Ayush Goyal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


Malaria is a hazardous disease responsible for nearly 400 to 1000 deaths annually in India. The conventional technique to diagnose malaria is through microscopy. It takes a few hours by for an expert to examine and diagnose malarial parasites in the blood smear. The diagnosis report may vary when the blood smears are analyzed by different experts. In proposed work, an image processing based robust algorithm is designed to diagnose malarial parasites with minimal intervention of an expert. Initially, the images are enhanced by extracting hue, saturation and intensity planes followed by histogram equalization. After preprocessing, a median filter is employed to eliminate the noise from the images. After the preprocessing, segmentation of malaria parasite is achieved using k-means clustering to get the clear vision of the region of interest. The clustering is followed by region growing area extraction to remove the unwanted area from the segmented image. The second part deals with counting the number of infected RBCs. The method uses roundness detection for the calculation of the infected RBCs. The experiments give encouraging results for the saturation plane of HSI color space and segmentation accuracy by up to 94%.


Malaria Thin blood smears Saturation plane clustering Region growing segmentation 


  1. 1.
    WHO Malaria Report (2015)Google Scholar
  2. 2.
    Nugroho, H.A., Akbar, S.A., Murhandarwati, E.E.H.: Feature extraction and classification for detection malaria parasites in thin blood smear. In: IEEE 2nd International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), October 2015Google Scholar
  3. 3.
    Mohammed, H.A., Abdelrahman, I.A.M.: Detection and classification of malaria in thin blood slide images. In: IEEE International Conference on Communication, Control, Computing, and Electronics Engineering (ICCCCEE) (2017)Google Scholar
  4. 4.
    Nanoti, A., Jain, S., Gupta, C., Vyas, G.: Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear. In: IEEE International Conference on Inventive Computation Technologies (ICICT), August 2016Google Scholar
  5. 5.
    Savkare, S.S., Narote, S.P.: Automatic system for classification of erythrocytes infected with malaria and identification of parasite’s life stage. Proc. Technol. 6, 405–410 (2012)CrossRefGoogle Scholar
  6. 6.
    Walliander, M., et al.: Automated segmentation of blood cells in Giemsa stained digitized thin blood films. Diagn. Pathol. 8, S37 (2012)CrossRefGoogle Scholar
  7. 7.
    Khan, W.: Image segmentation techniques: a survey. J. Image Graph. 2(1), 6–9 (2013)Google Scholar
  8. 8.
    Verma, A., Scholar, M.T., Lal, C., Kumar, S.: Image segmentation: review paper. Int. J. Educ. Sci. Res. Rev. 3(2) (2016)Google Scholar
  9. 9.
    Das, D.K., Maiti, A.K., Chakraborty, C.: Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears. J. Microsc. 257(3), 238–252 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sayantan Bhattacharya
    • 1
  • Anupama Bhan
    • 1
    Email author
  • Ayush Goyal
    • 2
  1. 1.Department of Electronics and Communication EngineeringAmity UniversityNoidaIndia
  2. 2.Frank H. Dotterweich College of Engineering, Texas A&M UniversityKingsvilleUSA

Personalised recommendations