Abstract
3D image visualization gives the detailed information of MR images. In this paper, 2D images of different patients have been taken and these are preprocessed, segmented and post-processed. The preprocessing steps include thresholding and skull stripping. Watershed algorithm is applied for segmentation, stacking is performed to arrange different slices of tumor in shape, and interpolation is done to get smoothness between different slices of tumor and lastly rendering (Phong shading applied) which gives realness to the shape of tumor. The developed strategy has been tested on MATLAB 2013a software platform. The dataset of different patients had been taken having no tumor, less tumor, and high tumor.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Murugavallii S, Rajamani V (2006) A high speed parallel fuzzy c-mean algorithm for brain tumor segmentation. BIME J 6(1):29–34
Gopal NN, Karnan M (2010) Diagnose brain tumor MRI using images processing clustering algorithms such as fuzzy C means along with intelligent optimisation techniques
Resmi A, Thomas T (2012) A semi-automatic method of segmentation and 3D modelling of glioma tumors from brain MRI. Biomed Sci Eng 5:378–383
Kavitha C, Sangeetha S (2013) Automatic multimodality brain tumor detection. Int J Emerg Technol Adv Eng 3(3):264–268
Madhikar G, Lokhande SS (2013) Brain tumor detection and classification by using modified region growing method: a review. IJERT 2(12):2316–2320
Singh K, Kaur G (2014) A comprehensive review of various medical image processing techniques for MRI images. Int J Adv Res Comput Sci Softw Eng 4(5):1069–1072
Yu C-P et al (2014) 3D blob based brain tumor detection and segmentation in MRI. Institute of Computing, University of Campinas, Brazil
Zin S, Khaing AS (2014) Brain tumor detection and segmentation using watershed segmentation and morphological operation. Int J Res Eng Technol 3(3):367–374
Sharma P, Singh H (2015) Improvement of brain tumor feature based segmentation using decision based alpha trimmed global mean filter. Int J Comput Appl 121(21):1–8
Sindhu A, Meera S (2015) A survey on detecting brain tumor in MR images using image processing techniques. Int J Innov Res Comput Commun Eng 3(1):1–8
Wakchaure SL, Khandekar A (2015) Visualization of 3d view of detected brain tumor and calculation of its volume. Int J Tech Res Appl 3(6):120–126
Lopes S, Jaiswal D (2015) A methodical approach for detection and 3D reconstruction of brain tumor in MRI. Int J Comput Appl 118(17):0975–0978
Vanitha U et al (2015) Tumor detection in brain using morphological image processing. J Appl Sci Eng Methodol 1(1):131–136
Abdel-Maksoud E et al (2015) Brain tumor segmentation based on a hybrid clustering technique. Egypt Inform J 16(1):71–81
Kaur H, Kaur S (2016) Improved brain tumor detection using object based segmentation. Int J Eng Trends Technol (IJETT) 13(1):1–6
Samriti et al (2016) Brain tumor detection using image segmentation. Int J Eng Trends Technol (IJETT) 4(2):1–3
Isselmou AEK et al (2016) A novel approach for brain tumor detection using MRI images. Biomed Sci Eng 9:41–52
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
Bhandari, K., Pal, B.L., Vaishnav, A. (2019). Representation of 3D View of Tumor from 2D Images Using Watershed Algorithm. In: Mishra, S., Sood, Y., Tomar, A. (eds) Applications of Computing, Automation and Wireless Systems in Electrical Engineering. Lecture Notes in Electrical Engineering, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-13-6772-4_92
Download citation
DOI: https://doi.org/10.1007/978-981-13-6772-4_92
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6771-7
Online ISBN: 978-981-13-6772-4
eBook Packages: EngineeringEngineering (R0)