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Background Modeling Using Temporal-Local Sample Density Outlier Detection Read chapter Read first chapter

2017 | OriginalPaper | Chapter

ROI Segmentation from Brain MR Images with a Fast Multilevel Thresholding

Authors : Subhashis Banerjee, Sushmita Mitra, B. Uma Shankar

Published in: Proceedings of International Conference on Computer Vision and Image Processing

Publisher: Springer Singapore

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Abstract

A novel region of interest (ROI) segmentation for detection of Glioblastoma multiforme (GBM) tumor in magnetic resonance (MR) images of the brain is proposed using a two-stage thresholding method. We have defined multiple intervals for multilevel thresholding using a novel meta-heuristic optimization technique called Discrete Curve Evolution. In each of these intervals, a threshold is selected by bi-level Otsu’s method. Then the ROI is extracted from only a single seed initialization, on the ROI, by the user. The proposed segmentation technique is more accurate as compared to the existing methods. Also the time complexity of our method is very low. The experimental evaluation is provided on contrast-enhanced T1-weighted MRI slices of three patients, having the corresponding ground truth of the tumor regions. The performance measure, based on Jaccard and Dice indices, of the segmented ROI demonstrated higher accuracy than existing methods.

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previous chapter Performance Analysis of Texture Image Retrieval in Curvelet, Contourlet, and Local Ternary Pattern Using DNN and ELM Classifiers for MRI Brain Tumor Images
next chapter Surveillance Scene Segmentation Based on Trajectory Classification Using Supervised Learning
Footnotes
1
“Brain tumor image data used in this work were obtained from the MICCAI 2012 Challenge on Multimodal Brain Tumor Segmentation (http://​www.​imm.​dtu.​dk/​projects/​BRATS2012) organized by B. Menze, A. Jakab, S. Bauer, M. Reyes, M. Prastawa, and K. Van Leemput. The challenge database contains fully anonymized images from the following institutions: ETH Zurich, University of Bern, University of Debrecen, and University of Utah.
Note: the images in this database have been skull stripped”.
 
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Metadata
Title
ROI Segmentation from Brain MR Images with a Fast Multilevel Thresholding
Authors
Subhashis Banerjee
Sushmita Mitra
B. Uma Shankar
Copyright Year
2017
Publisher
Springer Singapore
Book
Proceedings of International Conference on Computer Vision and Image Processing

Print ISBN: 978-981-10-2103-9

Electronic ISBN: 978-981-10-2104-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-2104-6_23