Skip to main content
SpringerLink
Log in

Image Processing in Biomedical Science

  • Chapter
  • First Online:
Advances in Soft Computing and Machine Learning in Image Processing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 730))

Abstract

Images have been of utmost importance in the life of humans as vision is one of the most important sense, therefore, images play a vital role in every individual’s perception. As a result, image processing from its very first application in the 1920s to till date has advanced many folds. There are various fields in which image processing flourished but one of the major and upcoming field is Medical Science. There has been a dramatic expansion in Medical Image Processing in last two decades due to its ever-increasing and non-ending applications. The main reason that the field evolved in such short time is because of its interdisciplinary nature, it attracts expertise from different background like Computer Science, Biotechnology, Statistics, Biology, etc. Computerised Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), X-Ray, Gamma Ray, and Ultrasound are some of the commonly used medical imaging technologies used today. The rush in the development of new technology demands to meet the challenges faced such as—how to improve the quality of an image, how to automate medical imaging and predictions, and how to expand its reach to all medical fields. The sole and only purpose of this chapter is to provide an introduction to medical image application and techniques so that more interest can be developed for further research in the same field.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Proch´azka A Vyˇsata, O.: History and biomedical application of digital signal and image processing, Paper presented at international workshop on computational intelligence for multimedia understanding, 1–2 Nov 2014

    Google Scholar 

  2. Gonzalez, R.C.,Woods, R.E.: Introduction. In: Marcia, J.H. (ed.) Digital Image Processing, 2nd edn., Addison Wesley, USA, 1987 (2004)

    Google Scholar 

  3. Elangovan, A., Jeyaseelan, T.: Medical imaging modalities: a survey. Presented at the IEEE international conference on emerging trends in engineering, technology and science, Pudukkottai, 24–26 Feb 2016

    Google Scholar 

  4. Dhawan, A.P.: Medical imaging modalities: X-ray imaging. In: Lajoh, H. (ed.) Medical Image Analysis, 2nd edn, pp. 79–98. Wiley, New Jersey (2011)

    Chapter  Google Scholar 

  5. Chou, K.Y., Lin, C.S., Chien, C.H., Chiang, J.S., Hsia, C.H.: Using statistical parametric contour and threshold segmentation technology Applied in X-ray bone images. Paper presented at the international symposium on intelligent signal processing and communication systems. 24–27 Oct 2016

    Google Scholar 

  6. Fysikopoulos, E., Kopsinis, Y., Georgiou, M., Loudos, G., et al.: A sub-sampling approach for data acquisition in gamma ray emission tomography 63(3), 1399–1407 (2016)

    Google Scholar 

  7. Dhawan, A.P.: Nuclear medicine imaging modalities. In: Lajoh, H. (ed.) Medical Image Analysis, 2nd edn, pp. 139–156. Wiley, New Jersey (2011)

    Chapter  Google Scholar 

  8. Gambhir, S.S.: Molecular imaging of cancer with positron emission tomography. Nat. Rev. Cancer 2, 683–693 (2002)

    Article  Google Scholar 

  9. Hubner, K.F., McDonald, T.W., Neithammer, J.G., Smith, T.G., Gould, H.R., Buonocore, E., et al.: Assessment of primary and metastatic ovarian cancer by Positron emission tomography (PET) using 2-[18F] deoxyglucose (2-[18F]FDG). J. Gynecol. Oncol. 51, 197–204 (1993)

    Article  Google Scholar 

  10. Dhawan, A.P.: Medical imaging modalities: ultrasound imaging. In: Lajoh, H. (ed.) Medical Image Analysis, 2nd edn, pp. 157–172. Wiley, New Jersey (2011)

    Chapter  Google Scholar 

  11. George, S., Cheng, R., Ignjatovic, Z.: A novel ultrasound imaging technique for portable and high speed imaging. Paper presented at the 13th International on new circuits and systems conference, 7–10 June 2015

    Google Scholar 

  12. Dhawan, A.P.: Medical imaging modalities: magnetic resonance imaging. In: Lajoh, H. (ed.) Medical Image Analysis, 2nd edn, pp. 99–138. Wiley, New Jersey (2011)

    Chapter  Google Scholar 

  13. Zhang, H., Han, H., Liang, Z., Hu, Y., Liu, Y., Moore, W., Ma, J., Lu, H., et al.: Extracting information from previous full-dose CT scan for knowledge-based Bayesian reconstruction of current low-dose CT images. Journal. IEEE transactions on medical imaging 35(3), 860–870 (2016)

    Article  Google Scholar 

  14. Tunga, P., Singh, V.: Extraction and description of tumour region from the brain mri image using segmentation techniques. Paper presented at the IEEE international conference on recent trends in electronics information communication technology, 20–21 May 2016

    Google Scholar 

  15. Guo, L., Wu, Y., Liu, X., Li, Y., Xu, G., Yan, W.: Threshold optimisation of adaptive template filtering for mri based on intelligent optimisation algorithm. Paper presented at the 28th IEEE annual international conference on engineering in medicine and biology society, New York City, 30 Aug–3 Sept 2006

    Google Scholar 

  16. Kaur, A.: A review paper on image segmentation and its various techniques in image processing. Intern. J. Sci. Res. 3(12), 12–14 (2012)

    Google Scholar 

  17. Masood, S., Sharif, M., Masood, A., Yasmin, M., Raza, M., et al.: A survey on medical image segmentation. Curr. Medical Imaging Rev. 11(1), 3–14 (2015)

    Article  Google Scholar 

  18. Thomas, H.M.W., Kumar, S.C.P.: A review of segmentation and edge detection methods for real time image processing used to detect brain tumour. Paper presented at the IEEE international conference computational intelligence and computing research, Madurai, 10–12 Dec 2015

    Google Scholar 

  19. Patil, D.D., Deore, S.G.: Medical Image Segmentation: A Review. Intern. J. Comput. Sci. Mob. Comp. 2(1), 22–27 (2013)

    Google Scholar 

  20. Gupta, G., Tiwari, S.: Boundary extraction of biomedical images using edge operators. Intern. J. Innovative Res. Comput. Commun. Eng. 3(11) (2015)

    Google Scholar 

  21. Fabijanska, A., Sankowski, D.: Edge detection in brain images. Paper presented at the IEEE international conference on perspective technologies and methods in mems design, Lviv, 21–24 May 2008

    Google Scholar 

  22. Norouzi, A., Rahim, M.S.M., Altameem, A., Saba, T., Rad, A.E., Rehman, A., Uddin, M., et al.: Medical image segmentation methods, algorithms, and applications. J. IETE Tech. Rev. 31(3), 199–213 (2014)

    Article  Google Scholar 

  23. Uchida, S.: Image processing and recognition for biological images. Develop. Growth Differ. (2013)

    Google Scholar 

  24. Siddique, I., Bajwa, I.S., Naveed, M.S., Choudhary, M.A.: Automatic functional brain mr image segmentation using region growing and seed pixel. Paper presented at the IEEE 4th international conference on information & communications technology, 10–12 Dec 2006

    Google Scholar 

  25. Oghli, M.G., Fallahi, A., Pooyan, M.: Automatic region growing method using gsmap and spatial information on ultrasound images. Paper presented at the IEEE 18th Iranian conference on electrical engineering, 11–13 May 2010

    Google Scholar 

  26. Adegoke, B.O., Olawale, B.O., Olabisi, N.I.: Overview of medical image segmentation. Intern. J. Eng. Res. Develop. 8(9), 13–17 (2013)

    Google Scholar 

  27. Poonguzhali, S., Ravindran, G.: A complete automatic region growing method for segmentation of masses on ultrasound images. Paper presented at the IEEE International conference on biomedical and pharmaceutical engineering, 11–14 Dec 2006

    Google Scholar 

  28. Kumbhar, A.D., Kulkarni, A.V.: Magnetic resonant image segmentation using trained k-means clustering. Paper presented at the IEEE world congress on information and communication technologies, 11–14 Dec 2011

    Google Scholar 

  29. Li, S.Z.: Low-level MRF. In: Singh, S.(ed.) Models Markov Random Field Modeling in Image Analysis, 3rd edn., pp. 49–90. Springer, London, (2009)

    Google Scholar 

  30. Chen, S., Cao, L., Wang, Y.: Image segmentation by MAP-ML estimations. IEEE Trans. Image Process. 19(9), 2254–2264 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  31. Wells, W.M., Grimson, W.E.M., Kikinis, S., Jolesz, F.A.: Adaptive segmentation of MRI data. IEEE Trans. Med. Imaging 15(4), 429–442 (1996)

    Article  Google Scholar 

  32. Bouhlel, N., Sevestre, S., Rajhi, H., Hamzad, R.: A New Markov random field model based on K-distribution for textured ultrasound image. Paper presented at medical imaging 2004: Ultrasonic imaging and signal processing, San Diego, CA, 14 Feb 2004

    Google Scholar 

  33. Li, S.Z.: Introduction. In: Singh, S (ed.) Models Markov Random Field Modeling in Image Analysis, 3rd edn., pp. 1–20. Springer, London (2009)

    Google Scholar 

  34. Chen, S., Cao, L., Liu, J., Tang, X.: Iterative MAP and ML estimations for image segmentation. Paper presented at IEEE conference on computer vision and pattern recognition, Minneapolis, USA, 17–22 June 2007

    Google Scholar 

  35. Liu, X., Yetik, I.S.: A maximum likelihood classification method for image segmentation considering subject variability. Paper presented at 2nd IEEE southwest symposium on image analysis & interpretation (SSIAI), Austin, USA, 23–25 May 2010

    Google Scholar 

  36. Li, S.Z.: MRF parameter estimation. In: Singh, S. (ed.) Models Markov Random Field Modeling in Image Analysis, 3rd edn., pp. 183–214 Springer, London (2009)

    Google Scholar 

  37. Metaxas, D.N.: Motion-based part segmentation and tracking. Physics-Based Deformable Models: Applications To Computer Vision, Graphics, and Medical Imaging, 1st edn., pp. 149–178. Kluwer Academic Publishers, New York (1997)

    Google Scholar 

  38. Gauch, J.M., Pien, H., Shah, J.: Hybrid deformable models for three-dimensional biomedical image segmentation. Paper presented at nuclear science symposium and medical imaging IEEE conference, 30 Oct–5 Nov 1994

    Google Scholar 

  39. Hegadi, R., Kop, A., Hangarge, M.: A survey on deformable model and its applications to medical imaging. J. IJCA Spec. Issue Recent Trends Image Proces. Pattern Recogn. 2(7), 64–75 (2010)

    Google Scholar 

  40. Heinz, D.: Hyper markov non-parametric processes for mixture modeling and model selection. Dissertation, Carnegie Mellon University (2010)

    Google Scholar 

  41. McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Med. Image Anal. 1(2), 91–108 (1996)

    Article  Google Scholar 

  42. Nixon, M.S., Aguado, A.S.: Low- level feature extraction (including edge Detection). Feature Extraction and Image Processing, 1st edn, pp. 99–160. Reed educational and professional publishing, Woburn (2002)

    Chapter  Google Scholar 

  43. Nixon, M.S., Aguado, A.S.: Feature extraction by shape matching. Feature Extraction and Image Processing, 1st edn, pp. 161–216. Reed educational and professional publishing, Woburn (2002)

    Chapter  Google Scholar 

  44. Nixon, M.S., Aguado, A.S.: Flexible shape extraction (snakes and other techniques). Feature Extraction and Image Processing, 1st edn, pp. 217–246. Reed educational and professional publishing, Woburn (2002)

    Chapter  Google Scholar 

  45. Mandloi, G.: A survey on feature extraction techniques for colour images. Intern. J. Comput. Sci. Info. Technol. 5(3), 4615–4620 (2014)

    Google Scholar 

  46. Beauchemin, S.S., Barron, J.L., et al.: The computation of optical flow. J. ACM Comput. Surv. 27(3) (1995)

    Google Scholar 

  47. Cootes, F.T., Edwards, G., Taylor, C.J.: A comparative evaluation of active appearance model algorithms. Paper presented at the 9th British machine vision conference (1998)

    Google Scholar 

  48. Smitha, P., Shaji, L., Mini, M.G.: A review of medical image classification techniques. Paper presented at international conference on VLSI, communication & instrumentation (2011)

    Google Scholar 

  49. Song, E., Pan, N., Hung, C.C., Li, X., Jin, L.: Reflection invariant local binary patterns for image texture classification. Paper presented at Racs proceedings of the ACM conference on research in adaptive and convergent systems, 9–12 Dec 2015

    Google Scholar 

  50. Shi, Z., He, L., Suzuki, K., Nakamura, T., Itoh, H., et al.: Survey on neural networks used for medical image processing. Int. J. Comput. Sci. 3(1), 86–100 (2009)

    Google Scholar 

  51. Jiang, J., Trundle, P., Ren, J., et al.: Medical image analysis with artificial neural networks. Comput. Med. Imaging Graph. 34(8), 617–631 (2010)

    Article  Google Scholar 

  52. Kim, K.I., Jung, K., Park, S.H., Kim, H.J., et al.: Support vector machines for texture classification. IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1542–1550 (2002)

    Article  Google Scholar 

  53. Suralkar, S.R., Karode, A.H., Pawade, P.W., et al.: Texture image classification using support vector machine. Int. J. Comp. Tech. Appl 3(1), 71–75 (2012)

    Google Scholar 

  54. Zhang, B.B., Zhou, H.P.: An improved SVM for aliasing dataset: demarcation threshold support vector machine. Paper presented at the IEEE 9th international symposium on computational intelligence and design, 10–11 Dec 2016

    Google Scholar 

  55. HemaRajini, N., Bhavani, R. Classification of MRI brain images using k-nearest neighbour and artificial neural network. Paper presented at the IEEE international conference on recent trends in information technology, 3–5 June 2011

    Google Scholar 

  56. Patidar, D., Jain, N., Parikh, A. Performance analysis of artificial neural network and K nearest neighbours image classification techniques with wavelet features. Paper presented at the IEEE international conference on computer communication and systems, 20–21 Feb 2014

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biotechnology Engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India

    Lakshay Bajaj & Yasha Hasija

  2. Department of Computer Science Engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India

    Kannu Gupta & Yasha Hasija

Authors
  1. Lakshay Bajaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kannu Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yasha Hasija
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasha Hasija .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. CUCEI, Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico

    Diego Alberto Oliva

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Bajaj, L., Gupta, K., Hasija, Y. (2018). Image Processing in Biomedical Science. In: Hassanien, A., Oliva, D. (eds) Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-319-63754-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63754-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63753-2

  • Online ISBN: 978-3-319-63754-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation