Skip to main content

Advertisement

SpringerLink
Log in

Escalate the efficacy of breast tumor detection through magnetic resonance imaging: a framework

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

In this era, breast cancer leads among the major cause of death facing by the women. To delineate the breast lesions accurately is a tough job because of their heterogeneous intensity distribution and convoluted structure. Breast MR imaging is being increasingly used for clinical settings to evaluate breast structure as an adjunct to conventional imaging modalities i.e. mammography, ultrasound etc. because of its 3-D stuff, non-invasiveness, and the conclusive soft tissue contrast between fibro-glandular tissues and fatty tissue. In this paper, the early detection and diagnosis process of breast tumor using MRI is elaborated which includes sections: pre-processing, segmentation, feature extraction/selection and classification. Initially, the noise removal or contrast enhancement is done under pre-processing. Due to the diversity of shapes of different types of tumor, the exact segmentation for description of abnormalities is still a challenge. In the next step, firstly extract the features and then select the appropriate ones. Lastly, the classifier classifies the images as normal or abnormal. The efficacy of any algorithm lies with the fact that the each step in the algorithm is determined by comparing various techniques to the extent to find out the best one for the breast MR images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Sushmi Dey (2016) Cancer cases in India likely to soar 25% by 2020; Indian Council of Medical Research, http://timesofindia.indiatimes.com/india/Cancer-cases-in-India-likely-to-soar-25-by-2020-CMR/Articleshow/52334632cm8s. Accessed Sept 2017

  2. Agha M et al (2016) 3T MRI of the breast with computer aided diagnosis. Alex J Med 52:9–18

    Article  Google Scholar 

  3. (2017)http://indiaturnspink.blogspot.in/2016/08/with-vision-of-better-tomorrow-india.html. Accessed Aug 2017

  4. Globocan (2012) Estimated cancer incidence, mortality and prevalence worldwide 2012; http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspxAccessed 10 Jan 2014

  5. Breast cancer in developing countries (2009) The Lancet 374(9701); 1567. http://download.thelancet.com/pdfs/journals/lancet/PIIS0140673609619309.pdf Accessed 10 Dec 2013

  6. Global Burden of Cancer (2013)https://scroll.in/article/733634/how-cancer-has-india-in-its-gripAcessed Jan 2018

  7. Indian council of medical research (2013) 3 year report of population based cancer registries 2009–2011. Indian council of medical research. http://www.ncdirindia.org/NCRP/all_ncrp_reports/pbcr_report_2012_2014/all_content/pdf_printed_version/preliminary_pages_printed.pdf. Accessed May 2018

  8. India turns pink, breast cancer free India 2030. http://indiaturnspink.org/breast-cancer/facts-and-statistics/. Accessed April 2018

  9. Jalalian et al (2017) Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI J 16:113–137

    Google Scholar 

  10. Kuhl CK (2007) Current status of breast MR imaging. Clin Appl Radiol 244:672–691

    Google Scholar 

  11. Kunnavil R et al (2016) Estimation of burden of female breast cancer in India from 2016 to 2026 using disability adjusted life year. Int J Community Med Public Health 3(5):1135–1140

    Article  Google Scholar 

  12. International Agency for Research on Cancers, World Health Organisation. https://www.iarc.fr/. Accessed July 2018

  13. Kilic et al (2012) Diagnostic MRI of the Breast. Eurasian J Med 44:106–114

    Article  Google Scholar 

  14. Jaglan P, Dass R, Duhan M (2019) Breast cancer detection techniques issues and challenges. J Inst Eng (India) 100:1–8. https://doi.org/10.1007/s40031-019-00391-2

    Article  Google Scholar 

  15. Lin et al (2015) Utilization of magnetic resonance imaging in breast cancer screening. Curr Oncol 22(5):332

    Article  Google Scholar 

  16. American Cancer Society. https://www.cancer.org/cancer/breast-cancer/screening-tests-and-early-detection/breast-mri-scans.html. Accessed July 2017

  17. Chen W, Giger ML, Lan L et al (2004) Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys 31:1076–1082

    Article  Google Scholar 

  18. Nattkemper TW, Arnrich B, Lichte O et al (2005) Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. Art Intell Med 34:129–139

    Article  Google Scholar 

  19. Hadjiiski et al (2006) Advances in CAD for diagnosis of breast cancer. Curr Opin Obstet Gynecol 18(1):64–70

    Article  Google Scholar 

  20. Gal Y et al (2009) Feature and classifier selection for automatic classification of lesions in dynamic contrast-enhanced MRI of the breast. In: 2009 digital image computing: techniques and applications, 1–3 Dec 2009. IEEE, Melbourne. https://doi.org/10.1109/DICTA.2009.29

  21. Bandyopadhyay SK et al. (2010) Digital imaging in pathology towards detection and analysis of human breast cancer; second International Conference on computational intelligence, communication systems and networks, 978-0-7695-4158-7/10

  22. Bozza G et al (2010) Application of no-sampling linear sampling method to breast cancer detection. IEEE Trans Biomed Eng 57(10):2525

    Article  Google Scholar 

  23. Mustaqeem A, Javed A, Fatima T (2012) An efficient brain tumor detection algorithm using watershed and thresholding based segmentation. MECS 10:34–39

    Google Scholar 

  24. Kanimozhi and Dhanalakshmi (2013) Automatic segmentation of brain tumor using K- means clustering and its area calculation. Int J Adv Electr Electron Eng 2(2):130–134

    Google Scholar 

  25. Joseph RP, Singh CS, Manikandan M (2014) Brain tumor MRI image segmentation and detection in image processing. Int J Res Eng Technol 3(1):1–5

    Article  Google Scholar 

  26. Benson CC, Lajish VL (2014) Morphology based enhancement and skull stripping of MRI brain images, 2014 International Conference on intelligent computing applications, 978-1-4799-3966-4/14

  27. Khare S, Guptc N, Srivastava V (2014) Optimization technique, curve fitting and machine learning used to detect brain tumor in MRI, IEEE International Conference on computer communication and systems

  28. Al-Tamimi MSH, Sulong G (2014) Tumor brain detection through mr images: a review of literature. J Theor Appl Inf Technol 62(2):387–403

    Google Scholar 

  29. Zaitoun NM, Aqel MJ (2015) Survey on image segmentation techniques. Procedia Comput Sci 65:797–806

    Article  Google Scholar 

  30. Jaglan P, Dass R, Duhan M (2019) Detection of breast cancer using MRI: a pictorial essay of the image processing techniques. Int J Comput Eng Res Trends (IJCERT) 6(1):238–245

    Google Scholar 

  31. Dass R, Priyanka, Devi S (2011) Speckle noise reduction techniques. Int J Electron Electr Eng (IJEEE) 16:47–57

    Google Scholar 

  32. Rajeshwar D, Swapna D, Priyanka (2012) Effect of Wiener–Helstrom filtering cascaded with bacterial foraging optimization to despeckle the ultrasound images. Int J Comput Sci Issues (I JCSI) 9(4):372–380

    Google Scholar 

  33. Dass Rajeshwar (2018) Speckle noise reduction of US images using BFO cascaded with Wiener filter and DWT in homomorphic region. Procedia Comput Sci J 132:1543–1551 (Elsevier)

    Article  Google Scholar 

  34. Jaglan P, Dass R, Duhan M (2018) A comparative analysis of various image segmentation techniques, Proceedings of 2nd International Conference on Communication, Computing and Networking. Lecture Notes in Networks and Systems, vol. 46, 359–374, Springer, Singapore

  35. Dass Rajeshwar, Priyanka V, Devi S (2012) Image segmentation techniques. Int J Electron Commun Technol (IJECT) 3(1):66–67

    Google Scholar 

  36. Dass R, Vikash (2013) Comparative analysis of threshold based, K-means and level set segmentation algorithms. Int J Comput Sci Technol (IJCST) 4(1):93–95

    Google Scholar 

  37. Haralick et al (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621

    Article  Google Scholar 

  38. Dhawan AP, Le Royer E (1988) Mammographic feature enhancement by computerized image processing. Comput Methods Programs Biomed 27(1):23–25

    Article  Google Scholar 

  39. Kim JK, Park HW (1999) Statistical textural features for detection of micro-calcifications in digitized mammograms. IEEE Trans Med Imag 18:231–238

    Article  Google Scholar 

  40. Yang SC et al (2005) A computer-aided system for mass detection and classification in digitized mammograms. Biomed Eng Appl Basis Commun 17(5):215–228

    Article  Google Scholar 

  41. Sheshadri & Kandaswamy (2007) Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. Comput Med Imaging Graph 31(1):46–48

    Article  Google Scholar 

  42. Roberto R, Pereira Jr et al. (2008) Computerized scheme for detection of diffuse lung diseases on CR chest images, Proceedings of SPIE—The International Society for Optical Engineering, vol. 69(15)

  43. Elfarra BK, Abuhaiba ISI (2013) New Feature extraction method for mammogram computer aided diagnosis. Intl J Signal Process Image Process Pattern Recognit 6(1):13–36

    Google Scholar 

  44. Dass Rajeshwar (2013) Sanjeet, effect of feedforward back propagation neural network for breast tumor classification. Int J Comput Sci Amp Technol (IJCST) 4(2):731–735

    Google Scholar 

  45. Dass R, Priyanka V (2012) Image segmentation performance evaluation methods. Int J Sc Eng Amp Comput Technol (IJSECT) 2(1):125–127

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ECED, Deenbandhu Chhottu Ram University of Science and Technology, Murthal, Sonipat, India

    Poonam Jaglan, Rajeshwar Dass & Manoj Duhan

Authors
  1. Poonam Jaglan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajeshwar Dass
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manoj Duhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Poonam Jaglan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaglan, P., Dass, R. & Duhan, M. Escalate the efficacy of breast tumor detection through magnetic resonance imaging: a framework. Int. j. inf. tecnol. 12, 879–887 (2020). https://doi.org/10.1007/s41870-019-00393-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-019-00393-9

Keywords

Search

Navigation