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Type P63 Digitized Color Images Performs Better Identification than Other Stains for Ovarian Tissue Analysis

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Articulated Motion and Deformable Objects (AMDO 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9756))

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Abstract

Ovarian reproductive tissues are responsible for human reproduction. It is important for the pathology experts to perform routine examination for ovarian reproductive tissues to prescribe necessary treatments for women who face conceiving complications. Manual microscopic analysis is considered the best analysis approach for experts in the laboratory as existing scanning modalities do not provide satisfactory results for identification. Due to longer processing time and observation variability between experts computer based approaches have become popular as it can reduce time and can identify the reproductive tissue accurately. In this paper a new modified approach is presented and comparative analysis were performed using existing computer based approaches on three different types of digitized images acquired from microscopic biopsy slides. Proposed new modified approach indicates acceptable accuracy rate in comparison to manual identification approach to analyze ovarian reproductive tissues.

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Acknowledgement

The authors would like to thank Assistant Professor and head of the department (Pathology) and domain expert Doctor Sadequel Islam Talukder (MBBS, M.Phil (Pathology), Shaheed Sayed Nazrul Islam Medical College, Kishoreganj, Bangladesh) for providing the test images, annotated images and necessary feature information.

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Authors and Affiliations

  1. Edith Cowan University, Joondalup, WA, Australia

    T. M. Shahriar Sazzad, L. J. Armstrong & A. K. Tripathy

  2. Don Bosco Institute of Technology, Mumbai, India

    A. K. Tripathy

Authors
  1. T. M. Shahriar Sazzad
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  2. L. J. Armstrong
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  3. A. K. Tripathy
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Corresponding author

Correspondence to T. M. Shahriar Sazzad .

Editor information

Editors and Affiliations

  1. UIB-Universitat de les Illes Balears, Palma de Mallorca, Spain

    Francisco José Perales

  2. University of Surrey, Guildford, United Kingdom

    Josef Kittler

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Shahriar Sazzad, T.M., Armstrong, L.J., Tripathy, A.K. (2016). Type P63 Digitized Color Images Performs Better Identification than Other Stains for Ovarian Tissue Analysis. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-41778-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41777-6

  • Online ISBN: 978-3-319-41778-3

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