ROI Segmentation from Brain MR Images with a Fast Multilevel Thresholding

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.

[1]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[2]  R. Gillies,et al.  Quantitative imaging in cancer evolution and ecology. , 2013, Radiology.

[3]  Jian Yu,et al.  Otsu Method and K-means , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[4]  Subhashis Banerjee,et al.  A Novel GBM Saliency Detection Model Using Multi-Channel MRI , 2016, PloS one.

[5]  Xinjian Chen,et al.  Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images , 2013, Medical Image Anal..

[6]  Sushmita Mitra,et al.  Medical image analysis for cancer management in natural computing framework , 2015, Inf. Sci..

[7]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[8]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[9]  Wenyu Liu,et al.  Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Yun-Chia Liang,et al.  An Automatic Multilevel Image Thresholding Using Relative Entropy and Meta-Heuristic Algorithms , 2013, Entropy.

[11]  Daniel Withey,et al.  A Review of Medical Image Segmentation: Methods and Available Software , 2008 .

[12]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[13]  Thomas E Yankeelov,et al.  Clinical utility of quantitative imaging. , 2015, Academic radiology.

[14]  Pau-Choo Chung,et al.  A Fast Algorithm for Multilevel Thresholding , 2001, J. Inf. Sci. Eng..

[15]  Ron Kikinis,et al.  Volumetric CT-based segmentation of NSCLC using 3D-Slicer , 2013, Scientific Reports.

[16]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[17]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[18]  Chia-Hung Wang,et al.  Optimal multi-level thresholding using a two-stage Otsu optimization approach , 2009, Pattern Recognit. Lett..

[19]  Serge Beucher,et al.  The Morphological Approach to Segmentation: The Watershed Transformation , 2018, Mathematical Morphology in Image Processing.