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Proceedings of International Conference on Recent Advancement on Computer and Communication pp 143–150Cite as

A Benign and Malignant Mass Classification Based on Second-Order Statistical Parameters at Different Offset

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 34))

Abstract

The objective of this paper is to investigate the classification of masses on digitized screening mammograms as benign or malignant with second-order statistical parameters. Many CAD tools were developed over the past two decades to help radiologists in detecting and diagnosing breast cancer. The proposed method removes and deletes unwanted signs present in the digitized mammogram sample’s background and applies enhancement process to eliminate noise and find the breast region. After that, segmentation phase is performed for automatic mass detection. From detected mass, second-order texture features from gray level co-occurrence matrix (GLCM) are extracted. These extracted parameters for different offset are plotted and based on the relation of direction’s plots; the mass is classified as benign or malignant.

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References

  1. Ganesan, K., Acharya, U., Chua, K.C., Min, L.C., Abraham, K.T., Ng, K.: Computer-aided breast cancer detection using mammograms: a review. IEEE Rev. Biomed. Eng. 6, 77–98 (2013)

    Article  Google Scholar 

  2. Bandyopadhyay, S.K., Maitra, I.K., Kim, T.: Identification of abnormal masses in digital mammography images. In: 2011 International Conference on Ubiquitous Computing and Multimedia Applications (UCMA), vol. 2, pp. 35–41. IEEE (2011)

    Google Scholar 

  3. Sara, D., Dezfooli, M.A.: A method for improve preprocessing images mammography. Int. J. Inf. Educ. Technol. 1(1), 90 (2011)

    Google Scholar 

  4. Bick, U., Diekmann, F.: Digital Mammography. Springer, Berlin (2009)

    Google Scholar 

  5. Hosseini, H.G., Fatemi, M., Alizad, A.: Registration of vibro-acoustography images and X-ray mammography. In: 27th Annual International Conference on Engineering in Medicine and Biology Society, 2005, IEEE-EMBS 2005, pp. 1846–1849. IEEE (2006)

    Google Scholar 

  6. Ponraj, D.N., Evangelin Jenifer, M., Poongodi, P., Samuel Manoharan, J.: A survey on the preprocessing techniques of mammogram for the detection of breast cancer. J. Emerg. Trends Comput. Inf. Sci. 2(12), 656–664 (2011)

    Google Scholar 

  7. Padmanabhan, S., Sundararajan, R.: Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis. In: 2012 International Conference on Machine Vision and Image Processing (MVIP), pp. 153–156. IEEE (2012)

    Google Scholar 

  8. Bandyopadhyay, S.K.: Detection of abnormal masses in mammogram images. Int. J. Comput. Sci. Inf. Technol. 1(1), 438–442 (2010)

    Google Scholar 

  9. Sharma, J., Rai, J.K., Tewari, R.P.: Identification of pre-processing technique for enhancement of mammogram images. In: 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), Greater Noida, pp. 115–119 (2014)

    Google Scholar 

  10. Ramani, R., Suthanthira Vanitha, N.: The pre-processing techniques for breast cancer detection in mammography images. MECS (http://www.mecs-press.org/) 5, 47–54 (2013)

    Google Scholar 

  11. Nagi, J., Abdul Kareem, S., Nagi, F., Khaleel Ahmed, S.: Automated breast profile segmentation for ROI detection using digital mammograms. In: IEEE EMBS Conference on Biomedical Engineering and Sciences, Kuala Lumpur, pp. 87–92 (2010)

    Google Scholar 

  12. Bandyopadhyay, S.K.: Pre-processing of mammogram images. Int. J. Eng. Sci. Technol. 2(11), 6753–6758 (2010)

    Google Scholar 

  13. Palkar, P., Agrawal, P.: Preprocessing of mammograms for computer-aided diagnosis of breast cancer. Int. J. Mod. Comput. Sci. 4(5), 59–62 (2016)

    Google Scholar 

  14. Palkar, P., Agrawal, P.: ROI detection in digital mammograms for detection of breast cancer. Int. J. Eng. Sci. Comput. 7(2), 4472–4475 (2017)

    Google Scholar 

  15. Sheng, L.: The analysis of digital mammograms: speculated tumor detection and normal mammogram characterization. Sch. Electr. Comput. Eng. (1999)

    Google Scholar 

  16. Palkar, P., Agrawal, P.: A technique to extract statistical parameters of digital mammogram to detect breast cancer. Int. J. Adv. Res. Sci. Eng. Technol. 3(12), 3033–3038 (2016)

    Google Scholar 

  17. Palkar, P., Agrawal, P.: An approach to analyse digital mammogram using statistical parameters. Int. J. Res. Appl. Sci. Eng. Technol. 5(1), 1–8 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. RTMNU, Nagpur, India

    Pravin Palkar

  2. G. H. Raisoni Academy of Engineering and Technology, Nagpur, India

    Pankaj Agrawal

Authors
  1. Pravin Palkar
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  2. Pankaj Agrawal
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Corresponding author

Correspondence to Pravin Palkar .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Technocrats Institute of Technology, Bhopal, Madhya Pradesh, India

    Basant Tiwari

  2. Computer Science and Engineering, DSPM IIIT, Naya Raipur, Chhattisgarh, India

    Vivek Tiwari

  3. Department of Mathematics, Sungkyunkwan University, Suwon, Korea (Republic of)

    Kinkar Chandra Das

  4. Microsoft Innovation Academy, Computer Science and Engineering, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  5. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish C. Bansal

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Palkar, P., Agrawal, P. (2018). A Benign and Malignant Mass Classification Based on Second-Order Statistical Parameters at Different Offset. In: Tiwari, B., Tiwari, V., Das, K., Mishra, D., Bansal, J. (eds) Proceedings of International Conference on Recent Advancement on Computer and Communication . Lecture Notes in Networks and Systems, vol 34. Springer, Singapore. https://doi.org/10.1007/978-981-10-8198-9_15

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  • DOI: https://doi.org/10.1007/978-981-10-8198-9_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8197-2

  • Online ISBN: 978-981-10-8198-9

  • eBook Packages: EngineeringEngineering (R0)

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