A review of characteristic study of micro calcification using son mammogram images

  • Authors

    • G R. Jothilakshmi
    • Dr Arun Raaza
    • Dr Y. Sreenivasa Varma
    • Dr V. Rajendran
    • R Guru Nirmal Raj
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.14171
  • Binning, Feature Extraction, Order-Statistic Filter, Signatures Calculation

  • In recent years, the mortality rate in women increases a lot due to occurrence of breast cancer. The challenging task in medical field is to find breast cancer at an early stage. Because the detection of breast cancer at its early stage makes the treatments easy and increases the survival rate of victim. In this review paper, a detailed explanation is given about the characteristics of micro calcification (ie its shape and distribu-tion) and detailed literature survey is done that shows the techniques used previously to detect the Micro calcification from different diag-nostic sources. Moreover, this review paper proposes a new algorithm, to detect micro calcification and to find its classification (benign or malignant) from sono mammogram images at an early stage. First filtering will be taken place in order to enhance the image. Second the image is to be binned and the affected area is to be separated (ROI) by edge detection. Then the size, shape, distribution and density of the ROI will be determined. From the characteristics of RoI, a mathematical model will be developed and signatures to be found to confirm the occurrence of micro calcification and to find its classifications.

     

     

     

  • References

    1. [1] Webpage Moose and Doc (http://breast-cancer.ca/)

      [2] Webpage American Cancer society (https://www.acr.org)

      [3] Webpage HTTP://GLOBOCAN.IARC.FR

      [4] Webpage DDSM http://marathon.csee.usf.edu/Mammography/Database.html)

      [5] Arai K, Abdullah IN, Okumura H, Kawakami R. Improvement of automated detection method for clustered microcalcification based on wavelet transformation and support vector machine. World. 2013 Apr; 1384:458.

      [6] Kim JK, Park HW. Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE transactions on medical imaging.1999 Mar 18 (3):231-8.

      [7] Dhawan AP, Chitre Y, Kaiser-Bonasso C. Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Transactions on Medical Imaging.1996 Jun; 15(3):246-59.

      [8] Scimeca M, Giannini E, Antonacci C, Pistolese CA, Spagnoli LG, Bonanno E. Microcalcifications in breast cancer: an active phenomenon mediated by epithelial cells with mesenchymal characteristics. BMC cancer.2014 Apr 23 14 (1): one.

      [9] Bezerra LA, Oliveira MM, Rolim TL, Conci A, Santos FG, Lyra PR, Lima RC. Estimation of breast tumor thermal properties using infrared images. Signal Processing.2013 Oct 31 93 (10):2851-63.

      [10] Mondal PP, Rajan K, Ahmad I. Filter for biomedical imaging and image processing. JOSA A. 2006 Jul 1; 23 (7):1678-86.

      [11] 11.EtehadTavakol M, Chandran V, Ng EY, Kafieh R. Breast cancer detection from thermal images using bispectral invariant features. International Journal of Thermal Sciences.2013 Jul 31; 69:21-36.

      [12] Arai K, Abdullah IN, Okumura H, Kawakami R. Improvement of automated detection method for clustered microcalcification based on wavelet transformation and support vector machine. World. 2013 Apr; 1384:458.

      [13] Baker R, Rogers KD, Shepherd N, Stone N. New relationships between breast microcalcifications and cancer. British journal of cancer.2010 Sep 28 103 (7):1034-9.

      [14] Yoo Y, Im J, Paik J. Low-Light Image Enhancement Using Adaptive Digital Pixel Binning. Sensors.2015 Jun 25 15 (7):14917-31.

      [15] Quintanilla-Dominguez J, Ojeda-Magaña B, Cortina-Januchs MG, Ruelas R, Vega-Corona A, Andina D. Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications. Scientia Iranica.2011 Jun 30; 18(3):580-9.

      [16] Malek AA, Rahman WE, Ibrahim A, Mahmud R, Yasiran SS, Jumaat AK. Region and boundary segmentation of microcalcifications using seed-based region growing and mathematical morphology. Procedia-Social and Behavioral Sciences.2010 Dec 31; 8:634-nine.

      [17] Eapena MM, Ancelita MS, Geetha G. Segmentation of Tumors from Ultrasound Images with PAORGB. Procedia Computer Science.2015 Dec 31; 50:663-eight.

      [18] Govardhan TV. Classical and Novel Diagnosis Techniques for Early Breast Cancer Detection–A Comparative Approach. International Journal of Engineering Mathematics and Computer Sciences.2015 Nov 11 3 (11):12-6.

      [19] Ponraj DN, Jenifer ME, Poongodi P, Manoharan JS. A survey on the preprocessing techniques of mammogram for the detection of breast cancer. Journal of Emerging Trends in Computing and Information Sciences.2011 Dec2 (12):656-64.

      [20] Verma B. A neural network based technique to locate and classify microcalcifications in digital mammograms. InNeural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on 1998 May 4 (Vol. 3, pp. 1790-1793). IEEE.

      [21] Shan J. A fully automatic segmentation method for breast ultrasound images (Doctoral dissertation, Utah State University).

      [22] Shan J, Cheng HD, Wang Y. A completely automatic segmentation method for breast ultrasound images using region growing. InProceedings of the ninth International Conference on Computer Vision, Pattern Recognition, and Image Processing 2008 Dec 5.

      [23] Inbarani HH. A novel neighborhood rough set based classification approach for medical diagnosis. Procedia Computer Science. 2015 Dec 31; 47:351 [9].

      [24] Rose RJ, Allwin S. Computerized cancer detection and classification using ultrasound images: a survey. Int. J. Eng. Res. Dev. 2013 Jan; 5:36-47.

      [25] Shareef SR. Breast cancer detection based on watershed transformation. IJCSI International Journal of Computer Science Issues.2014 Jan; 11 (1):237-45.

      [26] Cahoon TC, Sutton MA, Bezdek JC. Breast cancer detection using image-processing techniques. InFuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on 2000 (Vol. 2, pp. 973-976). IEEE.

      [27] Salvado J, Roque B. Detection of calcifications in digital mammograms using wavelet analysis and contrast enhancement. InIEEE International Workshop on Intelligent Signal Processing, 2005.2005 Sep 1 (pp. 200-205). IEEE.

      [28] Dominguez AR, Nandi AK. Enhanced multi-level thresholding segmentation and rank based region selection for detection of masses in mammograms. In2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07 2007 Apr 15 (Vol. 1, pp. I-449). IEEE.

      [29] Strickland RN, Hahn HI. Wavelet transforms for detecting microcalcifications in mammograms. IEEE Transactions on Medical Imaging.1996 Apr 15 (2):218-29.

      [30] Hahn HI. Wavelet transforms for detecting microcalcifications in mammography.

      [31] Strickland RN, Hahn HI. Wavelet transform matched filters for the detection and classification of microcalcifications in mammography. In Image Processing, 1995. Proceedings. International Conference on 1995 Oct 23 (Vol. 1, pp. 422-425). IEEE.

      [32] Tang J, Sun Q, Agyepong K. An image enhancement algorithm based on a contrast measure in the wavelet domain for screening mammograms. In2007 IEEE International Conference on Image Processing 2007 Sep 16 (Vol. 5, pp. V-29). IEEE.

  • Downloads

  • How to Cite

    R. Jothilakshmi, G., Arun Raaza, D., Y. Sreenivasa Varma, D., V. Rajendran, D., & Guru Nirmal Raj, R. (2018). A review of characteristic study of micro calcification using son mammogram images. International Journal of Engineering & Technology, 7(2.33), 290-294. https://doi.org/10.14419/ijet.v7i2.33.14171