Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Novel Intelligent Multiple Watermarking Schemes for the Protection of the Information Content of a Document Image Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Brain Tumor Segmentation from Multimodal MR Images Using Rough Sets

Authors : Rupsa Saha, Ashish Phophalia, Suman K. Mitra

Published in: Computer Vision, Graphics, and Image Processing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Automatic segmentation of brain tumors from Magnetic Resonance images is a challenging task due to the wide variation in intensity, size, location of tumors in images. Defining a precise boundary for a tumor is essential for diagnosis and treatment of patients. Rough set theory, an extension of classical set theory, deals with the vagueness of data by determining the boundary region of a set. The aim of this work is to explore the possibility and effectiveness of using a rough set model to represent the tumor regions in MR images accurately, with Quadtree partitioning and simple K-means as precursors to indicate and limit the possible relevant regions. The advantage of using rough sets lie in its ability to represent the impreciseness of set boundaries, which is one of the major challenges faced in tumor segmentation. Experiments are carried out on the BRATS 2013 and 2015 databases and results are comparable to those reported by recent works.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter A Hybrid Deep Architecture for Face Recognition in Real-Life Scenario
next chapter A Text Recognition Augmented Deep Learning Approach for Logo Identification
Literature
1.
Khosla, D.: Concurrent therapy to enhance radiotherapeutic outcomes in glioblastoma. Ann. Transl. Med. 4(3) (2016)
2.
Krupa, K., Bekiesińska-Figatowska, M.: Artifacts in magnetic resonance imaging. Pol. J. Radiol. 80, 93 (2015)CrossRef
3.
Schild, H.H.: MRI Made Easy. Berlex Laboratories, Whippany (1999)
4.
Vovk, U., Pernuš, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 26(3), 405–421 (2007)CrossRef
5.
Christine Fennema-Notestine, I., Ozyurt, B., Clark, C.P., Morris, S., Bischoff-Grethe, A., Bondi, M.W., Jernigan, T.L., Fischl, B., Segonne, F., Shattuck, D.W., et al.: Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location. Hum. Brain Mapp. 27(2), 99–113 (2006)CrossRef
6.
Stadlbauer, A., Moser, E., Gruber, S., Buslei, R., Nimsky, C., Fahlbusch, R., Ganslandt, O.: Improved delineation of brain tumors: an automated method for segmentation based on pathologic changes of 1 H-MRSI metabolites in gliomas. Neuroimage 23(2), 454–461 (2004)CrossRef
7.
Kaus, M.R., Warfield, S.K., Nabavi, A., Black, P.M., Jolesz, F.A., Kikinis, R.: Automated segmentation of MR images of brain tumors. Radiology 218(2), 586–591 (2001)CrossRef
8.
Gibbs, P., Buckley, D.L., Blackband, S.J., Horsman, A.: Tumour volume determination from mr images by morphological segmentation. Phys. Med. Biol. 41(11), 2437 (1996)CrossRef
9.
Nakhmani, A., Kikinis, R., Tannenbaum, A.: MRI brain tumor segmentation and necrosis detection using adaptive sobolev snakes. In: SPIE Medical Imaging, p. 903442. International Society for Optics and Photonics (2014)
10.
Gordillo, N., Montseny, E., Sobrevilla, P.: State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31(8), 1426–1438 (2013)CrossRef
11.
Hall, L.O., Bensaid, A.M., Clarke, L.P., Velthuizen, R.P., Silbiger, M.S., Bezdek, J.C.: A comparison of neural network, fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Transactions on Neural Networks 3(5), 672–682 (1992)
12.
Zhou, J., Chan, K.L., Chong, V.F.H., Krishnan, S.M.: Extraction of brain tumor from MR images using one-class support vector machine. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society. IEEE-EMBS 2005, pp. 6411–6414. IEEE (2006)
13.
Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., Cuadra, M.B.: A review of atlas-based segmentation for magnetic resonance brain images. Comput. Methods Prog. Biomed. 104(3), e158–e177 (2011)CrossRef
14.
Hirano, S., Tsumoto, S.: Segmentation of medical images based on approximations in rough set theory. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS, vol. 2475, pp. 554–563. Springer, Heidelberg (2002). doi:10.​1007/​3-540-45813-1_​73 CrossRef
15.
Maji, P., Roy, S.: Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation. PLoS ONE 10(4), e0123677 (2015)CrossRef
16.
Pawlak, Z.: Rough sets. Int. J. Parallel Program. 11, 341–356 (1982)MATH
17.
Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging, 33 (2014)
18.
Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11), e245 (2013)CrossRef
19.
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRef
20.
MICCAI. MICCAI Brain Tumor Image Segmentation Challenge-BRATS, Boston, Massachusetts, September 2014
Metadata
Title
Brain Tumor Segmentation from Multimodal MR Images Using Rough Sets
Authors
Rupsa Saha
Ashish Phophalia
Suman K. Mitra
Copyright Year
2017
Book
Computer Vision, Graphics, and Image Processing

Print ISBN: 978-3-319-68123-8

Electronic ISBN: 978-3-319-68124-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68124-5_12

Premium Partner

    Image Credits