Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images

Keerthana Prasad, Jan Winter, Udayakrishna M. Bhat, Raviraja V. Acharya, Gopalakrishna K. Prabhu

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

This paper describes development of a decision support system for diagnosis of malaria using color image analysis. A hematologist has to study around 100 to 300 microscopic views of Giemsa-stained thin blood smear images to detect malaria parasites, evaluate the extent of infection and to identify the species of the parasite. The proposed algorithm picks up the suspicious regions and detects the parasites in images of all the views. The subimages representing all these parasites are put together to form a composite image which can be sent over a communication channel to obtain the opinion of a remote expert for accurate diagnosis and treatment. We demonstrate the use of the proposed technique for use as a decision support system by developing an android application which facilitates the communication with a remote expert for the final confirmation on the decision for treatment ofmalaria. Our algorithm detects around 96% of the parasites with a false positive rate of 20%. The Spearman correlation r was 0.88 with a confidence interval of 0.838 to 0.923, p<0.0001.

Original languageEnglish
Pages (from-to)542-549
Number of pages8
JournalJournal of Digital Imaging
Volume25
Issue number4
DOIs
Publication statusPublished - 08-2012

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Decision support systems
Image analysis
Malaria
Parasites
Blood
Communication
Color image processing
Color
Confidence Intervals
Composite materials
Infection

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

Cite this

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abstract = "This paper describes development of a decision support system for diagnosis of malaria using color image analysis. A hematologist has to study around 100 to 300 microscopic views of Giemsa-stained thin blood smear images to detect malaria parasites, evaluate the extent of infection and to identify the species of the parasite. The proposed algorithm picks up the suspicious regions and detects the parasites in images of all the views. The subimages representing all these parasites are put together to form a composite image which can be sent over a communication channel to obtain the opinion of a remote expert for accurate diagnosis and treatment. We demonstrate the use of the proposed technique for use as a decision support system by developing an android application which facilitates the communication with a remote expert for the final confirmation on the decision for treatment ofmalaria. Our algorithm detects around 96{\%} of the parasites with a false positive rate of 20{\%}. The Spearman correlation r was 0.88 with a confidence interval of 0.838 to 0.923, p<0.0001.",
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Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images. / Prasad, Keerthana; Winter, Jan; Bhat, Udayakrishna M.; Acharya, Raviraja V.; Prabhu, Gopalakrishna K.

In: Journal of Digital Imaging, Vol. 25, No. 4, 08.2012, p. 542-549.

Research output: Contribution to journalArticle

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