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Automatic Pattern Recognition for Detection of Disease from Blood Drop Stain Obtained with Microfluidic Device

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Advances in Signal Processing and Intelligent Recognition Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 425))

Abstract

This paper investigates automatic detection of disease from the pattern of dried micro-drop blood stains from a patient’s blood sample. This method has the advantage of being substantially cost-effective, painless and less-invasive, quite effective for disease detection in newborns and the aged. Disease has an effect on the physical properties of blood, which in turn affect the deposition patterns of dried blood micro-droplets. For example, low platelet count will result in thinning of blood, i.e. a change in viscosity, one of the physical properties of blood. Hence, the blood micro-drop stain patterns can be used for diagnosing diseases. This paper presents automatic analysis of the dried micro-drop blood stain patterns using computer vision and pattern recognition algorithms. The patterns of micro-drop blood stains of normal non-diseased individuals are clearly distinguishable from the patterns of micro-drop blood stains of diseased individuals. As a case study, the micro-drop blood stains of patients infected with tuberculosis have been compared to the micro-drop blood stains of normal non-diseased individuals. The paper delves into the underlying physics behind how the deposition pattern of the dried micro-drop blood stain is formed. What has been observed is a thick ring like pattern in the dried micro-drop blood stains of non-diseased individuals and thin contour like lines in the dried micro-drop blood stains of patients with tuberculosis infection. The ring like pattern is due to capillary flow, an outward flow carrying suspended particles to the edge. Concentric rings (caused by inward Marangoni flow) and central deposits are some of the other patterns that were observed in the dried micro-drop blood stain patterns of normal non-diseased individuals.

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Author information

Authors and Affiliations

  1. Department of MAE, ASET, Amity University, Noida, 201313, U.P., India

    Basant S. Sikarwar & Mukesh Roy

  2. Department of EEE, ASET, Amity University, Noida, 201313, U.P., India

    Priya Ranjan

  3. Department of ECE, ASET, Amity University, Noida, 201313, U.P., India

    Ayush Goyal

Authors
  1. Basant S. Sikarwar
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  2. Mukesh Roy
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  3. Priya Ranjan
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  4. Ayush Goyal
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Corresponding author

Correspondence to Basant S. Sikarwar .

Editor information

Editors and Affiliations

  1. and Management – Kerala (IIITM-K), Indian Inst. of Information Technology, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  3. Department of Electrical, Ryerson University, Toronto, Ontario, Canada

    Sri Krishnan

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. Computer Engineering Department, Vladimir State University, Vladimir Region, Russia

    Sergey Mosin

  6. School of Electrical, Nanyang Technological University, Singapore, Singapore

    Maode Ma

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© 2016 Springer International Publishing Switzerland

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Sikarwar, B.S., Roy, M., Ranjan, P., Goyal, A. (2016). Automatic Pattern Recognition for Detection of Disease from Blood Drop Stain Obtained with Microfluidic Device. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_56

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  • DOI: https://doi.org/10.1007/978-3-319-28658-7_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28656-3

  • Online ISBN: 978-3-319-28658-7

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