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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gonzalez FF, Miller SP (2006) Does perinatal asphyxia impair cognitive function without cerebral palsy? Arch Dis Child Fetal Neonatal Ed 91(6):F454–F459
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
Tatiraju S, Mehta A (2008) Image segmentation using k means clustering, EM and normalized cuts. Technical report, University of California Irvine
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
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
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
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
Moeskops P et al (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35(5)
Dass R, Priyanka DS (2013) Image segmentation techniques. Int J Electron Commun Technol 3(1):66–70
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
Prashantha, S.J., Poornima, K.M. (2019). Automatic Pattern Discovery of Neonatal Brain Tumor Segmentation and Abnormalities in MRI Sequence. In: Sridhar, V., Padma, M., Rao, K. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-13-5802-9_10
Download citation
DOI: https://doi.org/10.1007/978-981-13-5802-9_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5801-2
Online ISBN: 978-981-13-5802-9
eBook Packages: EngineeringEngineering (R0)