Advertisement

Development in Malaria and Anemia Screening: Medical Imaging Informatics Approach

  • Dev Kumar Das
  • Chandan Chakraborty
  • Rashmi Mukherjee
  • Ashok K. Maiti
Chapter

Abstract

Medical imaging informatics (MII) includes problems of image data representation and abstraction. This provides immense help not only in standardization and interoperability but also enhances image data usability for data mining, decision support, and visual modeling and simulation. Hematological research has been significantly substantiated with the advancement of medical informatics approach. Among various hematological disorders, malaria and anemia are very common diseases that affect the human population as major health burden. This book chapter focuses on the quantitative evaluation of erythrocytes (red blood cells, RBCs) for characterization of malaria parasites and its differential infections. Anemic erythrocytes have also been recognized from light microscopic images with respect to their shape, size, and other quantitative attributes.

Keywords

Blood pathology Malaria Anaemia Microscopic image analysis Classification models 

References

  1. Branstetter BF (2007a) Basics of imaging informatics. Part 1. Radiology 243(3):656–667CrossRefPubMedGoogle Scholar
  2. Branstetter BF (2007b) Basics of imaging informatics: Part 2. Radiology 244(1):78–84CrossRefPubMedGoogle Scholar
  3. Brinkley JF (1991) Structural informatics and its applications in medicine and biology. Acad Med 66(10):589–591CrossRefPubMedGoogle Scholar
  4. Das DK et al (2013) Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia. J Microsc 249(2):136–149CrossRefPubMedGoogle Scholar
  5. Das DK et al (2015a) Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears. J Microsc 257(3):238–252CrossRefPubMedGoogle Scholar
  6. Das DK et al (2015b) Computational microscopic imaging for malaria parasite detection: a systematic review. J Microsc 260(1):1–19CrossRefPubMedGoogle Scholar
  7. DevKumar D et al (2012) Textural pattern classification of microscopic images for malaria screening. Advances in therapeutic engineering. CRC Press, Boaco Raton, pp 419–446Google Scholar
  8. Duda RO et al (2001) Pattern classification. Wiley, New YorkGoogle Scholar
  9. Ghosh M et al (2010) Automated leukocyte recognition using fuzzy divergence. Micron 41(7):840–846CrossRefPubMedGoogle Scholar
  10. Ghosh M et al (2011) Development of Renyi’s entropy based fuzzy divergence measure for leukocyte segmentation. J Med Imaging Health Inform 1:334–340CrossRefGoogle Scholar
  11. Gonzalez RC, Woods RE (2008) Digital image processing. Prentice Hall, New YorkGoogle Scholar
  12. Hohne KH et al (1995) A new representation of knowledge concerning human anatomy and function. Nat Med 1(6):506–511CrossRefPubMedGoogle Scholar
  13. Kumar S et al (2006) A rule-based approach for robust clump splitting. Pattern Recogn 39(6):1088–1098CrossRefGoogle Scholar
  14. Mejino JLV et al (2001) Symbolic modeling of structural relationships in the foundational model of anatomy. KR 2004 Workshop on formal biomedical knowledge representationGoogle Scholar
  15. Mukherjee R et al (2010) Clinical biomarker for predicting preeclampsia in women with abnormal lipid profile: statistical pattern classification approach. Systems in Medicine and Biology (ICSMB)Google Scholar
  16. Purwar Y et al (2011) Automated and unsupervised detection of malarial parasites in microscopic images. Malar J 10:364CrossRefPubMedPubMedCentralGoogle Scholar
  17. Raviraja S et al (2007) In: Ibrahim F, Osman NAA, Usman J, Kadri NA (eds) Analysis of detecting the malarial parasite infected blood images using statistical based approach, 3rd Kuala Lumpur international conference on biomedical engineering 2006: Biomed 2006, 11 †14 December 2006 Kuala Lumpur, Malaysia. Springer Berlin Heidelberg, Berlin/Heidelberg, pp 502–505Google Scholar
  18. Raviraja S et al (2008) In: Abu Osman NA, Ibrahim F, Wan Abas WAB, Abdul Rahman HS, Ting H-N (eds) A novel technique for malaria diagnosis using invariant moments and by image compression, 4th Kuala Lumpur international ronference on biomedical engineering 2008: BIOMED 2008 25†28 June 2008 Kuala Lumpur, Malaysia. Springer Berlin Heidelberg, Berlin/Heidelberg, pp 730–733Google Scholar
  19. Robb R, Hanson D (1995) The analyze software system for visualization and analysis in surgery simulation. In: Lele SR, Richtsmeier JT (eds) Computer integrated surgery. MIT Press, Cambridge, MA, pp 175–190Google Scholar
  20. Sinha U et al (2002) A review of medical imaging informatics. Ann N Y Acad Sci 980:168–197CrossRefPubMedGoogle Scholar
  21. Sio SW et al (2007) Malaria count: an image analysis-based program for the accurate determination of parasitemia. J Microbiol Methods 68(1):11–18CrossRefPubMedGoogle Scholar
  22. Taylor RH et al (1998) Computer-integrated surgery. Technol Clin Appl Clin Orthop Relat Res 354:5–7CrossRefGoogle Scholar
  23. Webb AR (2003) Introduction to statistical pattern recognition. Statistical pattern recognition. Wiley, pp 1–31Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Dev Kumar Das
    • 1
  • Chandan Chakraborty
    • 1
  • Rashmi Mukherjee
    • 2
  • Ashok K. Maiti
    • 3
  1. 1.School of Medical Science & TechnologyIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.RNLKWC, Vidyasagar UniversityMidnapurIndia
  3. 3.Medipath Clinic (P) Ltd.West MedinipurIndia

Personalised recommendations