Skip to main content
EXTENDED SEARCH
Log in
Top
Published in:
Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Automatic Pattern Discovery of Neonatal Brain Tumor Segmentation and Abnormalities in MRI Sequence

Authors : S. J. Prashantha, K. M. Poornima

Published in: Emerging Research in Electronics, Computer Science and Technology

Publisher: Springer Singapore

Log in

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

search-config
loading …

Abstract

Nowadays, image segmentation performs a large-scale contribution in the medical images. Magnetic resonance (MR) image is a good substantial strategy during the fetal and neonatal periods that make earlier diagnosis of individual tumor analysis. It is used as topmost image investigation model for pattern discovery of brain tumor. The aim of our study is to discriminate the unique images related to every one of the patterns of the brain tumor on neonatal MRI. Our proposed system consists of four steps. The first step is to explore the acquisition of the neonatal MRI sequence. The second step performs segmentation of the locality of abnormality. The third step is the segregation of tumor from edema and its enhanced sequences. The fourth step is the estimation of the drawn area of tumor and calculation of the mean. Our proposed method successfully detects the tumor area in the given neonatal MRI images.

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

Springer Professional "Business + Economics & Engineering + Technology"

Online-Abonnement

Springer Professional "Business + Economics & Engineering + Technology" gives you access to:

  • more than 102.000 books
  • more than 537 journals

from the following subject areas:

  • Automotive
  • Construction + Real Estate
  • Business IT + Informatics
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Mechanical Engineering + Materials
  • Insurance + Risk


Secure your knowledge advantage now!

inform now

Springer Professional "Engineering + Technology"

Online-Abonnement

Springer Professional "Engineering + Technology" gives you access to:

  • more than 67.000 books
  • more than 390 journals

from the following specialised fileds:

  • Automotive
  • Business IT + Informatics
  • Construction + Real Estate
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Mechanical Engineering + Materials





 

Secure your knowledge advantage now!

inform now
previous chapter Limited Number of Materials Scene Reconstruct from Mojette Projections
next chapter Theme-Based Partitioning Approach to Decision Tree: An Extended Experimental Analysis
Literature
1.
Gonzalez FF, Miller SP (2006) Does perinatal asphyxia impair cognitive function without cerebral palsy? Arch Dis Child Fetal Neonatal Ed 91(6):F454–F459
2.
Glavan CC, Holban S (2013) Segmentation of bone structure in X-ray images using convolutional neural network. Adv Electr Comput Eng 13(1):1–8
3.
Tatiraju S, Mehta A (2008) Image segmentation using k means clustering, EM and normalized cuts. Technical report, University of California Irvine
4.
Funmilola A, Oke OA, Adedeji TO, Alade OM, Adewusi EA (2012) Fuzzy K-C-means clustering algorithm for medical image segmentation. J Inf Eng Appl 2(6):21–32
5.
Kucuk N et al (2012) Tumor cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans Med Imaging 31(3):790–804
6.
Islam S, Ahmed M (2013) Implementation of image segmentation for natural images using clustering methods. Int J Emerg Technol Adv Eng 3(3):175–180
7.
Hammed SA et al (2012) Framework for enhancement of image guided surgery: finding area of tumor volume. Aust J Basic Appl Sci 6(1):9–16. ISSN 1991-8174
8.
Moeskops P et al (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35(5)
9.
Dass R, Priyanka DS (2013) Image segmentation techniques. Int J Electron Commun Technol 3(1):66–70
Metadata
Title
Automatic Pattern Discovery of Neonatal Brain Tumor Segmentation and Abnormalities in MRI Sequence
Authors
S. J. Prashantha
K. M. Poornima
Copyright Year
2019
Publisher
Springer Singapore
Book
Emerging Research in Electronics, Computer Science and Technology

Print ISBN: 978-981-13-5801-2

Electronic ISBN: 978-981-13-5802-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-5802-9_10