The Analysis of Medical Images

Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Medical images are different from other pictures in that they depict distributions of various physical features measured from the human body. They show attributes that are otherwise inaccessible. Furthermore, analysis of such images is guided by very specific expectations which gave rise to acquiring the images in the first place. This has consequences on the kind of analysis and on requirements for algorithms that carry out some or all of the analysis. Image analysis as part of the clinical workflow will be discussed in this chapter as well as the types of tools that exist to support the development and carrying out such an analysis. We will conclude with an example for the solution of an analysis task in order to illustrate important aspects for the development of methods for analyzing medical images.

Keywords

Assure Photography 

References

  1. Admasu F, Al-Zubi S, Toennies KD, Bodammer N, Hinrichs H (2003) Segmentation of multiple sclerosis lesions from MR brain images using the principles of fuzzy-connectedness and artificial neuron networks. In: Proceedings of international conference on image processing (ICIP 2003), pp 1081–1084Google Scholar
  2. Al-Zubi S, Toennies KD, Bodammer N, Hinrichs H (2002) Fusing markov random fields with anatomical knowledge and shape based analysis to segment multiple sclerosis white matter lesions in magnetic resonance images of the brain.In: Proceeding of SPIE (Medical Imaging 2002), vol 4684, pp 206–215Google Scholar
  3. Bankman IN (2008) Handbook of medical image processing and analysis, 2nd edn. Academic PressGoogle Scholar
  4. Dhawan AP (2011) Medical image analysis. IEEE Press Series on Biomedical Engineering, 2nd edn. WileyGoogle Scholar
  5. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward SR, Miller JV, Pieper S, Kikinis R (2012) 3d slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341CrossRefGoogle Scholar
  6. Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577CrossRefGoogle Scholar
  7. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJWL, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30(9):1234–1248CrossRefGoogle Scholar
  8. Nakamura S (2016) GNU OCTAVE primer for beginners: EZ guide to the commands and graphics, 2nd edn. CreateSpace Independent Publishing PlatformGoogle Scholar
  9. Paragios N, Duncan J, Ayache N (2015) Handbook of biomedical imaging: methodologies and clinical research. SpringerGoogle Scholar
  10. Preim B, Klemm P, Hauser H, Hegenscheid K, Oeltze S, Toennies KD, Völzke H (2016) Visual analytics of image-centric cohort studies in epidemiology. In: Visualization in medicine and life sciences III. Springer, pp 221–248Google Scholar
  11. Reyes‐Aldasoro, CC (2015) Biomedical image analysis recipes in MATLAB: for life scientists and engineers. WileyGoogle Scholar
  12. Toennies KD, Gloger O, Rak M, Winkler C, Klemm P, Preim B, Völzke H (2015) Image analysis in epidemiological applications. Inf Technol 57(1):22–29Google Scholar
  13. Udupa JK, Samarasekera S (1996) Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Models Image Process 58(3):246–261CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Computer Science Department, ISGOtto-von-Guericke-Universität MagdeburgMagdeburgGermany

Personalised recommendations