Study of Hardware Implementation on Size of the Microcalcification Detection Using Embedded Systems

  • Authors

    • G R. Jothilakshmi
    • P Mohana Priya
    • V K. Suvithra
    2018-08-15
    https://doi.org/10.14419/ijet.v7i3.27.17988
  • Microcalcification, binning of image, reflection coefficient, mass density, intensity, mammogram image.

  • Detection of microcalcification in glandular breasts is highly critical for early stage cancer detection since, it is very small in size. To detect such smaller microcalcification a hardware device is needed, which is created by the using the digital mammography image from DDSM database the image of malignant breast is acquainted. Two levels of binning is carried out with respect to the RoI to calculate the range of reflection coefficient. Linear mapping of reflection coefficient with mass density is projected as 3D and simultaneously the size of respective second bin is  calculated to derive the size if the microcalcification .This process is then implemented on hardware to make it more commercial for the people to detect the cancer at an early stage.

     

  • References

    1. [1] Kumar PS, Prabhakar P & Aneesh RP, “Depth Segmentation Method for Cancer Detection in Mammography Imagesâ€, International Journal on Recent and Innovation Trends in Computing and Communication, Vol.3, No.2, (2015), pp.606-612.

      [2] Jothilakshmi GR, “Identification of Micro Calcification through its Physical Characteristics Using Mammogram Imagesâ€, Journal of Advanced Research in Dynamical and Control Systems, (2017).

      [3] Jothilakshmi GR, Sharmila P & Raaza A, “Mammogram Segmentation using Region based Method with Split and Merge Techniqueâ€, Indian Journal of Science and Technology, Vol.9, No.40,(2016).

      [4] Zhang Y, Tomuro N, Furst J & Raicu DS, “Image enhancement and edge-based mass segmentation in mammographyâ€, International journal on research and innovation trends in computing and communication, (2015).

      [5] Balakumaran T, Vennila I & Gowrishankar C, “Detection of microcalcification in digital mammograms using one dimensional wavelet transformâ€, International Journal of Computer Science and Information Security, (2010), pp.99-104.

      [6] Jothilakshmi GR, Christilda RJ, Raaza A, SreenivasaVarma Y & Rajendran V, “Extracting region of interest using distinct block processing method in sono-mammogram imagesâ€, International Conference on Computer, Communication and Signal Processing (ICCCSP), (2017), pp.1-7.

      [7] Vishwanatha M, “Mammography image enhancement technique for detecting breast cancerâ€, International journal on research and innovation Trends in computing and communication Journal, (2014).

      [8] Sangeetha NM, “Detection of breast calcification in digital mammogram using image processing techniqueâ€, Journal of Network communication emerging technique, (2013).

      [9] Charate AP, “Mammogram image analysis for breast cancer detectionâ€, International journal on research and innovation Trends in computing and communication, (2015).

      [10] Machado P, Eisenbrey JR, Cavanaugh B & Forsberg F, “New Image Processing Technique for Evaluating Breast Microcalcificationsâ€, Journal of Ultrasound in Medicine, Vol.31, No.6,(2012), pp.885-893.

      [11] Balakumar T, “Detection of micro calcification in digital mammogram using one dimensional wavelet transformationâ€, IEEE journal, (2014).

      [12] Jothilakshmi GR, “Effective detection of mass abnormality and its classification using multi-SVM classificationâ€, International journal of science and technology, (2014).

      [13] Bethapudi P & Reddy ES, “Detection of Malignancy in Digital Mammograms from Segmented Breast Region Using Morphological Techniquesâ€, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), Vol.5, No.4,(2013), pp.9-12.

      [14] Naranje S, “Detection of breast cancer using ANNâ€, International journal on research and innovation Trends in computing and communication, (2014).

      [15] Shanmugavadivu P, Sivakumar V & Suhanya J, “Wavelet Transformation-Based Detection of Masses in Digital Mammogramsâ€, IJRET:International Journal of Research in Engineering and Technology, Vol.3, No.2, (2014), pp.131-138.

      [16] Ranjitha S, “Design and FPGA implementation of contrast enhancement on mammogram images for early detection of breast cancerâ€, JRITC, (2014).

      [17] Ramprabha T & Sathya priya T, “A Comparative Study on the Methods Used for the Detection of Breast Cancerâ€, International Journal on Recent and Innovation Trends in Computing and Communication, Vol.5, No.9, (2017), pp.143–147.

      [18] Singh N, Mohapatra AG & Kanungo G, “Breast cancer mass detection in mammograms using K-means and fuzzy C-means clusteringâ€, International Journal of Computer Applications, Vol.22, No.2, (2011).

      [19] Z Yesembayeva (2018). Features of the legal status of judges: Kazakhstan experience and foreign realities Opción, Año 33. 447-474

      [20] A Mukanbetkaliyev, S Amandykova, Y Zhambayev, Z Duskaziyeva, A Alimbetova (2018). The aspects of legal regulation on staffing of procuratorial authorities of the Russian Federation and the Republic of Kazakhstan Opción, Año 33. 187-216

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    R. Jothilakshmi, G., Mohana Priya, P., & K. Suvithra, V. (2018). Study of Hardware Implementation on Size of the Microcalcification Detection Using Embedded Systems. International Journal of Engineering & Technology, 7(3.27), 415-420. https://doi.org/10.14419/ijet.v7i3.27.17988