Enactment Valuation of Carcinoma with Segmentation Methods by Statistical Analysis

Enactment Valuation of Carcinoma with Segmentation Methods by Statistical Analysis



  • 2020

  • November-December

  • Research

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    Carcinoma is the most deadly disease of which Lung cancer
    and Breast cancer are of high risk. This approach target at diagnosing
    carcinoma by considering certain techniques. In this approach, a
    mammogram image and microscopic Lung image are considered. These
    images are applied through different image segmentation techniques.
    Later, Binarization technique is applied to improve the contrast of the
    images within the affected area. Median filter is used for removing noise
    within the image. To the noise-free images, some of the statistical
    parameters are calculated. Correlation is calculated between the
    reference parameters and cancerous parameters. These approaches are
    done for the detection of cancer in statistical approach. Results are
    processed using MATLAB and Xilinx.
    Keywords: Segmentation, Binarization, Carcinoma, Mammogram,
    Mean, Variance, Standard Deviation.

    [1] Cancer facts & figures 2018, American cancer society, https://www.cancer.org/
    [2] A to Z list of Cancers, https://www.cancer.gov/
    [3] A. Jemal, R. Siegel, E.Ward, Y. Hao, J. Xu, and M. J. Thun,“Cancer statistics, 2009,” CA: Cancer Journal for Clinicians,vol.
    59, no. 4, pp. 225–249, 2009.
    Welch, H. G., Prorok, P. C., O’Malley, A. J., & Kramer, B. S. (2016). Breast-cancer tumor size, overdiagnosis, and mammography
    effectiveness. New England Journal of Medicine, 375(15), 1438-1447.
    [4] Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., ... & Van Der Laak, J. (2016). Deep learning
    as a tool for increased accuracy and efficiency of histopathological diagnosis. Scientific reports, 6, 26286.
    [5] Kumar R., Srivastava R., Srivastava S. “Detection and Classification of Cancer from Microscopic Biopsy Images Using
    Clinically Significant and Biologically Interpretable Features” Proc of Journal of Medical Engineering, Volume 2015 (2015),
    Article ID 457906, 14 pages.
    [6] Srivaramangai, R., Hiremath, P., & Patil, A. S. (2017). Preprocessing mri images of colorectal cancer. International Journal of
    Computer Science Issues (IJCSI), 14(1), 48.
    [7] Garg, N. (2013). Binarization Techniques used for grey scale images. International Journal of Computer Applications, 71(1).
    [8] Bafna, Y., Verma, K., Panigrahi, L., & Sahu, S. P. (2018). Automated boundary detection of breast cancer in ultrasound images
    using watershed algorithm. In Ambient communications and computer systems (pp. 729-738). Springer, Singapore.
    [9] Hou, Z., Hu, Q., & Nowinski, W. L. (2006). On minimum variance thresholding. Pattern Recognition Letters, 27(14), 1732-1743.
    Basim Alhadidi (2007). Mammogram BreastCancer Image Detection Using ImageProcessing Functions, Information Technology
    Journal, Vol.6, No.2, pp.217-221.
    [10] Kumar, R., Arthanari, M., & Sivakumar, M. (2011). Image segmentation using discontinuity-based approach. Int. J. Multimedia
    Image Process, 1, 72-78.

    Enactment Valuation of Carcinoma with Segmentation Methods by Statistical Analysis