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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Tuberculosis diagnosis through image processing

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    12201058,13301139,13101095,12201099_CSE.pdf (1.406Mb)
    Date
    2017
    Publisher
    BRAC University
    Author
    Kamal, Nahid
    Islam, Subrina
    Asad, Al Faysal Bin
    Khatun, Sumaiya
    Metadata
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    URI
    http://hdl.handle.net/10361/8716
    Abstract
    Tuberculosis is most common contagious disease. Nowadays, millions of human beings of the world are suffering from it. We will use the most used worldwide method Ziehl-Neelsen stain (ZN-stain) to detect Tuberculosis which is based on sputum examination microscopically. This method needs expert human resources and implicit examination. The main constraints are expertise human,time and cost to implement our process. We will use Thresholding, multi-stage, color-based Bayesian segmentation identified possible ‘Tuberculosis objects’, removed artifacts by shape comparison and color-labeled objects as ‘definite’, ‘possible’ or ‘non-Tuberculosis’, bypassing photomicrographic calibration.In our work, we will use an algorithm based on image processing is developed for identification of Tuberculosis.
    Keywords
    Tuberculosis; Ziehl-Neelsen stain; Bayesian segmentation; Tuberculosis objects; Photomicrographic calibration; Image processing
     
    Description
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 41-42).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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