Research on Detection of Mycobacterium Tuberculosis from Microscopic Sputum Smear Images using Image Segmentation

被引:0
|
作者
Saravanan, D. [1 ]
Bhavya, R. [1 ]
Archanaa, G. I. [1 ]
Karthika, D. [1 ]
Subban, Ravi [2 ]
机构
[1] IFET Coll Engn & Technol, Dept Comp Sci & Engn, Villupuram, India
[2] Pondicherry Univ, Sch Engn & Technol, Dept Comp Sci, Pondicherry, India
关键词
color space; image segmentation; classification models; DIAGNOSIS; ANTIGENS; BACTERIA;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Tuberculosis (TB) is a deadly disease caused by a bacteria called Mycobacterium tuberculosis that can affect lungs mostly. Besides it is a disease that affects not only lungs in the body of a human being but other parts also. There are few diseases that kills the human population. The commonly used tools that can detect TB are: GeneXpert, microscopy, tuberculin bacilli test (TST), chest X-ray, interferon-y release assay (IGRA), and culture test. But the sputum smear microscopy is the mostly used tool. The merits of using this tool are: it is supposed to be easier, faster and cheaper than other methods without requiring much technical expertise and yielding sufficiently fast results. But the problem with this tool is that the screening may fail to detect TB and requires some eye strain with concentration. The automatic screening method will remove this problem. This paper provides research review on TB detection using image processing techniques.
引用
收藏
页码:969 / 974
页数:6
相关论文
共 50 条
  • [11] Automatic Detection of Tuberculosis bacilli from Conventional Sputum Smear Microscopic Images Using Densely Connected Convolutional Networks
    Panicker R.O.
    Sabu M.K.
    SN Computer Science, 3 (4)
  • [12] Detection of Overlapping Tuberculosis Bacilli in Sputum Smear Images
    Sheeba, Feminna
    Thamburaj, Robinson
    Mammen, Joy John
    Nithish, R.
    Karthick, S.
    7TH WACBE WORLD CONGRESS ON BIOENGINEERING 2015, 2015, 52 : 54 - 56
  • [13] Segmentation and Classification of Tuberculosis Bacilli from ZN-stained Sputum Smear Images
    Makkapati, Vishnu
    Agrawal, Ravindra
    Acharya, Raviraja
    2009 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, 2009, : 217 - +
  • [14] Simplified detection of Mycobacterium tuberculosis in sputum using smear microscopy and PCR with molecular beacons
    Haldar, Sagarika
    Chakravorty, Soumitesh
    Bhalla, Manpreet
    De Majumdar, Shyamasree
    Tyagi, Jaya Sivaswami
    JOURNAL OF MEDICAL MICROBIOLOGY, 2007, 56 (10) : 1356 - 1362
  • [15] Automatic Detection of Tuberculosis Bacilli from Microscopic Sputum Smear Images Using Faster R-CNN, Transfer Learning and Augmentation
    El-Melegy, Moumen
    Mohamed, Doaa
    ElMelegy, Tarek
    PATTERN RECOGNITION AND IMAGE ANALYSIS, PT I, 2020, 11867 : 270 - 278
  • [16] DETECTION OF TUBERCULOSIS BACILLI FROM ZIEHL NEELSON STAINED SPUTUM SMEAR IMAGES
    Sugirtha, Evangelin G.
    Murugesan, G.
    Vinu, S.
    2017 INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND EMBEDDED SYSTEMS (ICICES), 2017,
  • [17] Automatic Methods for Mycobacterium Detection on Stained Sputum Smear Images: a Survey
    Mithra K.S.
    Sam Emmanuel W.R.
    Pattern Recognition and Image Analysis, 2018, 28 (2) : 310 - 320
  • [18] DETECTION OF TUBERCULOSIS IN SPUTUM SMEAR IMAGES USING TWO ONE-CLASS CLASSIFIERS
    Khutlang, Rethabile
    Krishnan, Sriram
    Whitelaw, Andrew
    Douglas, Tania S.
    2009 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1 AND 2, 2009, : 1007 - +
  • [19] Microscopic detection of Mycobacterium tuberculosis in direct or processed sputum smears
    de Oliveira Magalhaes, Jose Luiz
    da Costa Lima, Juliana Figueiredo
    de Araujo, Ana Albertina
    Coutinho, Ilyana Oliveira
    Leal, Nilma Cintra
    Paiva de Almeida, Alzira Maria
    REVISTA DA SOCIEDADE BRASILEIRA DE MEDICINA TROPICAL, 2018, 51 (02) : 237 - 239
  • [20] Metaheuristic-Based Optimization Methods for the Segmentation of Tuberculosis Sputum Smear Images
    Priya, E.
    INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH, 2022, 13 (01)