Image Analysis Approach for Development of a Decision Support System for Detection of Malaria Parasites in Thin Blood Smear Images.
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 t...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 4; pp. 542 - 550 |
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| Autores principales: | , , , , |
| Formato: | algorithm pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104470704&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104470704 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2012 vid: 25 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104470704 77493905 10.1007/s10278-011-9442-6 NLM22146834 PMC3389088 104470704 ppf: 542 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Image Analysis Approach for Development of a Decision Support System for Detection of Malaria Parasites in Thin Blood Smear Images. aug: au: Prasad, Keerthana Winter, Jan Bhat, Udayakrishna Acharya, Raviraja Prabhu, Gopalakrishna affil: Manipal Centre for Information Science, Manipal University, Manipal 576104 India sug: subj: Malaria Diagnosis Decision Support Techniques Telemedicine Image Processing, Computer Assisted False Positive Results Microscopy Color Algorithms Spearman's Rank Correlation Coefficient P-Value Data Analysis Software Human ab: 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 of malaria. 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. pubtype: Academic Journal doctype: algorithm pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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