Abstract
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%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
WHO Malaria Report (2015)
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 2015
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)
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 2016
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)
Walliander, M., et al.: Automated segmentation of blood cells in Giemsa stained digitized thin blood films. Diagn. Pathol. 8, S37 (2012)
Khan, W.: Image segmentation techniques: a survey. J. Image Graph. 2(1), 6–9 (2013)
Verma, A., Scholar, M.T., Lal, C., Kumar, S.: Image segmentation: review paper. Int. J. Educ. Sci. Res. Rev. 3(2) (2016)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bhattacharya, S., Bhan, A., Goyal, A. (2020). Hybrid Segmentation of Malaria-Infected Cells in Thin Blood Slide Images. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_90
Download citation
DOI: https://doi.org/10.1007/978-3-030-16660-1_90
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16659-5
Online ISBN: 978-3-030-16660-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)