Advertisement

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

  • Poonam JaglanEmail author
  • Rajeshwar Dass
  • Manoj Duhan
Original Research
  • 5 Downloads

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.

Keywords

Classification Filtering Magnetic resonance imaging Segmentation Sensitivity Specificity 

References

  1. 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. 2.
    Agha M et al (2016) 3T MRI of the breast with computer aided diagnosis. Alex J Med 52:9–18CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Globocan (2012) Estimated cancer incidence, mortality and prevalence worldwide 2012; http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspxAccessed 10 Jan 2014
  5. 5.
    Breast cancer in developing countries (2009) The Lancet 374(9701); 1567. http://download.thelancet.com/pdfs/journals/lancet/PIIS0140673609619309.pdf Accessed 10 Dec 2013CrossRefGoogle Scholar
  6. 6.
    Global Burden of Cancer (2013)https://scroll.in/article/733634/how-cancer-has-india-in-its-gripAcessed Jan 2018
  7. 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. 8.
    India turns pink, breast cancer free India 2030. http://indiaturnspink.org/breast-cancer/facts-and-statistics/. Accessed April 2018
  9. 9.
    Jalalian et al (2017) Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI J 16:113–137Google Scholar
  10. 10.
    Kuhl CK (2007) Current status of breast MR imaging. Clin Appl Radiol 244:672–691Google Scholar
  11. 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):1135–1140CrossRefGoogle Scholar
  12. 12.
    International Agency for Research on Cancers, World Health Organisation. https://www.iarc.fr/. Accessed July 2018
  13. 13.
    Kilic et al (2012) Diagnostic MRI of the Breast. Eurasian J Med 44:106–114CrossRefGoogle Scholar
  14. 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 CrossRefGoogle Scholar
  15. 15.
    Lin et al (2015) Utilization of magnetic resonance imaging in breast cancer screening. Curr Oncol 22(5):332CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Chen W, Giger ML, Lan L et al (2004) Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys 31:1076–1082CrossRefGoogle Scholar
  18. 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 34:129–139CrossRefGoogle Scholar
  19. 19.
    Hadjiiski et al (2006) Advances in CAD for diagnosis of breast cancer. Curr Opin Obstet Gynecol 18(1):64–70CrossRefGoogle Scholar
  20. 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. 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, 978-0-7695-4158-7/10Google Scholar
  22. 22.
    Bozza G et al (2010) Application of no-sampling linear sampling method to breast cancer detection. IEEE Trans Biomed Eng 57(10):2525CrossRefGoogle Scholar
  23. 23.
    Mustaqeem A, Javed A, Fatima T (2012) An efficient brain tumor detection algorithm using watershed and thresholding based segmentation. MECS 10:34–39Google Scholar
  24. 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):130–134Google Scholar
  25. 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):1–5CrossRefGoogle Scholar
  26. 26.
    Benson CC, Lajish VL (2014) Morphology based enhancement and skull stripping of MRI brain images, 2014 International Conference on intelligent computing applications, 978-1-4799-3966-4/14Google Scholar
  27. 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 systemsGoogle Scholar
  28. 28.
    Al-Tamimi MSH, Sulong G (2014) Tumor brain detection through mr images: a review of literature. J Theor Appl Inf Technol 62(2):387–403Google Scholar
  29. 29.
    Zaitoun NM, Aqel MJ (2015) Survey on image segmentation techniques. Procedia Comput Sci 65:797–806CrossRefGoogle Scholar
  30. 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):238–245Google Scholar
  31. 31.
    Dass R, Priyanka, Devi S (2011) Speckle noise reduction techniques. Int J Electron Electr Eng (IJEEE) 16:47–57Google Scholar
  32. 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):372–380Google Scholar
  33. 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) CrossRefGoogle Scholar
  34. 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, SingaporeGoogle Scholar
  35. 35.
    Dass Rajeshwar, Priyanka V, Devi S (2012) Image segmentation techniques. Int J Electron Commun Technol (IJECT) 3(1):66–67Google Scholar
  36. 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):93–95Google Scholar
  37. 37.
    Haralick et al (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621CrossRefGoogle Scholar
  38. 38.
    Dhawan AP, Le Royer E (1988) Mammographic feature enhancement by computerized image processing. Comput Methods Programs Biomed 27(1):23–25CrossRefGoogle Scholar
  39. 39.
    Kim JK, Park HW (1999) Statistical textural features for detection of micro-calcifications in digitized mammograms. IEEE Trans Med Imag 18:231–238CrossRefGoogle Scholar
  40. 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):215–228CrossRefGoogle Scholar
  41. 41.
    Sheshadri & Kandaswamy (2007) Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. Comput Med Imaging Graph 31(1):46–48CrossRefGoogle Scholar
  42. 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)Google Scholar
  43. 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):13–36Google Scholar
  44. 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):731–735Google Scholar
  45. 45.
    Dass R, Priyanka V (2012) Image segmentation performance evaluation methods. Int J Sc Eng Amp Comput Technol (IJSECT) 2(1):125–127Google Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.ECEDDeenbandhu Chhottu Ram University of Science and TechnologySonipatIndia

Personalised recommendations