Abstract
The discovery of X-ray in 1895 initiated the era of medical imaging diagnostics. Since then, medical imaging systems have realized unprecedented advancements. These systems have also turned out to be invaluable tools in the practice of diagnostic medicine. However, despite the significant development in medical imaging technologies, processing medical images still pose a substantial challenge especially when it comes to image segmentation. That problem is gradually being alleviated by the implementation of digital medical image processing, especially in the diagnosis and treatment of brain tumors. But the capability of most of the contemporary image delineating algorithms remains limited. Therefore, there is a need to come up with the new medical image segmentation programs to fully utilize the power of digital image processing. In light that, this article reviews some of the contemporary algorithmic protocols for brain tumor delineation systems and how effective they are.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Deserno T (2009) Medical image processing. In: Optipedia. SPIE Press, Bellingham, WA
Dougherty G (2009) Digital image processing for medical applications. Cambridge University Press, Cambridge, UK
Aslam A, Khan E, Beg MS (2015) Improved edge detection algorithm for brain tumor segmentation. Proc Comput Sci 58:430–437. https://doi.org/10.1016/j.procs.2015.08.057
Kircher MF, De la Zerda A, Jokerst JV, Zavaleta CL, Kempen PJ, Mittra E, Gambhir SS (2012) A brain tumor molecular imaging strategy using a new triple-modality MRI-photoacoustic-Raman nanoparticle. Nat Med 18(5):829–834. https://doi.org/10.1038/nm.2721
Cheng Y, Morshed RA, Auffinger B, Tobias AL, Lesniak MS (2014) Multifunctional nanoparticles for brain tumor imaging and therapy. Adv Drug Deliv Rev 66:42–57. https://doi.org/10.1016/j.addr.2013.09.006
Despotović I, Goossens B, Philips W (2015) MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015:1–23. https://doi.org/10.1155/2015/450341
Luo Y, Liu L, Huang Q, Li X (2017) A novel segmentation approach combining region- and edge-based information for ultrasound images. Biomed Res Int 2017:1–18. https://doi.org/10.1155/2017/9157341
Meyer-Baese A, Plant C, Gorriz Saez JM (2014) Advanced computer vision approaches in biomedical image analysis. Comput Math Methods Med 2014:1–2. https://doi.org/10.1155/2014/347265
Nuster R, Slezak P, Paltauf G (2014) High resolution three-dimensional photoacoustic tomography with CCD-camera based ultrasound detection. Biomed Opt Express 5(8):2635. https://doi.org/10.1364/boe.5.002635
Sharma N, Aggarwal L (2010) Automated medical image segmentation techniques. J Med Phys 35(1):3–14. Retrieved from http://www.jmp.org.in/text.asp?2010/35/1/3/58777
Walter T, Shattuck DW, Baldock R, Bastin ME, Carpenter AE, Duce S, Hériché J (2010) Visualization of image data from cells to organisms. Nat Methods 7(3):S26–S41. https://doi.org/10.1038/nmeth.1431
Wang D, Wu Y, Xia J (2016) Review on photoacoustic imaging of the brain using nanoprobes. Neurophotonics 3(1):010901. https://doi.org/10.1117/1.nph.3.1.010901
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Anusha Linda Kostka, J.E. (2019). A Review of the Medical Image Segmentation Algorithms. In: Peng, SL., Dey, N., Bundele, M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-13-7150-9_30
Download citation
DOI: https://doi.org/10.1007/978-981-13-7150-9_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7149-3
Online ISBN: 978-981-13-7150-9
eBook Packages: EngineeringEngineering (R0)