Glioma extraction from MR images employing Gradient Based Kernel Selection Graph Cut technique

Medical imaging is one of the most daunting, challenging, and emerging research topics in image processing. Segmenting the glioma from the brain magnetic resonance images (MRI) is an important and demanding task, as it assists the medical experts for the disease diagnosis process. Recent research methods in image segmentation have highlighted the prospective of graph-based techniques for medical applications. As graph cut (GC) method is interactive in nature, it requires manual selection of the initial kernels for processing. The popularity of the GC method is limited by the occurrence of small cuts due to its shrinkage behavior leading to inaccurate extraction causing erroneous regions. This paper addresses the open research issue of shrinkage behavior by proposing the gradient based kernel selection (GBKS) GC method emphasizing on the directive inclination of the intensity scales. The proposed technique aids in the initialization of GC, removes the shrinkage problem, and locates the tumor in brain images without any human intervention. The performance results of the proposed GBKS GC method are evaluated on high-grade glioma and low-grade glioma MRI images and are analyzed and compared by using various measures. All the results present a remarkable improvement with GBKS GC technique over other existing techniques.

[1]  Zhimin Huo,et al.  Automated segmentation of breast lesions in ultrasound images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[2]  Xuelong Li,et al.  High-Order Energies for Stereo Segmentation , 2016, IEEE Transactions on Cybernetics.

[3]  Guillaume Lavoué,et al.  A comparative study of existing metrics for 3D-mesh segmentation evaluation , 2010, The Visual Computer.

[4]  Ewa Pietka,et al.  Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis , 2015, Comput. Medical Imaging Graph..

[5]  Thomas Bashford-Rogers,et al.  An evaluation of power transfer functions for HDR video compression , 2018, The Visual Computer.

[6]  Brian B. Avants,et al.  Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR , 2014, Neuroinformatics.

[7]  S. Bauer,et al.  A survey of MRI-based medical image analysis for brain tumor studies , 2013, Physics in medicine and biology.

[8]  Hui Huang,et al.  Learning a convolutional neural network for propagation-based stereo image segmentation , 2018, The Visual Computer.

[9]  Martin Welk,et al.  3-D Graph Cut Segmentation with Riemannian Metrics to Avoid the Shrinking Problem , 2011, MICCAI.

[10]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[11]  Ian H. Jermyn,et al.  Globally Optimal Regions and Boundaries as Minimum Ratio Weight Cycles , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Ling Shao,et al.  Sub-Markov Random Walk for Image Segmentation , 2016, IEEE Transactions on Image Processing.

[13]  Xuelong Li,et al.  Lazy Random Walks for Superpixel Segmentation , 2014, IEEE Transactions on Image Processing.

[14]  Ling Shao,et al.  Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm , 2016, IEEE Transactions on Image Processing.

[15]  Wenguan Wang,et al.  Higher-Order Image Co-segmentation , 2016, IEEE Transactions on Multimedia.

[16]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[17]  Ling Shao,et al.  Higher Order Energies for Image Segmentation , 2017, IEEE Transactions on Image Processing.

[18]  Dinggang Shen,et al.  Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis , 2014, MICCAI.

[19]  Nadia Magnenat-Thalmann.  Welcome to the year 2018 , 2017, The Visual Computer.

[20]  David Dagan Feng,et al.  Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation , 2018, The Visual Computer.

[21]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[22]  Stephen R. Aylward,et al.  Low-Rank to the Rescue - Atlas-Based Analyses in the Presence of Pathologies , 2014, MICCAI.

[23]  Jianhua Wang,et al.  Multi-class indoor semantic segmentation with deep structured model , 2017, The Visual Computer.

[24]  Martin Jägersand,et al.  3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set , 2012, International Journal of Computer Assisted Radiology and Surgery.

[25]  Vicent Caselles,et al.  Texture-Oriented Anisotropic Filtering and Geodesic Active Contours in Breast Tumor Ultrasound Segmentation , 2007, Journal of Mathematical Imaging and Vision.

[26]  Hiroshi Ishikawa,et al.  Higher-order clique reduction in binary graph cut , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Christos Davatzikos,et al.  A low cost approach for brain tumor segmentation based on intensity modeling and 3D Random Walker , 2015, Biomed. Signal Process. Control..

[28]  Wilfried Philips,et al.  MRI Segmentation of the Human Brain: Challenges, Methods, and Applications , 2015, Comput. Math. Methods Medicine.

[29]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Jyrki Lötjönen,et al.  Robust whole-brain segmentation: Application to traumatic brain injury , 2015, Medical Image Anal..

[31]  Christos Davatzikos,et al.  Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features , 2017, Scientific Data.

[32]  Vladimir Kolmogorov,et al.  Joint optimization of segmentation and appearance models , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  Mukesh M. Goswami,et al.  A comprehensive study of features used for brian tumor detection and segmentation from Mr images , 2017, 2017 Innovations in Power and Advanced Computing Technologies (i-PACT).

[34]  Xinjian Chen,et al.  A Survey of Graph Cuts/Graph Search Based Medical Image Segmentation , 2018, IEEE Reviews in Biomedical Engineering.

[35]  Victor Alves,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.

[36]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[37]  Baozong Yuan,et al.  Markerless human body motion capture using Markov random field and dynamic graph cuts , 2008, The Visual Computer.

[38]  Ben Glocker,et al.  Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR , 2012, MICCAI.

[39]  Shruti Jain,et al.  Segmentation of MR Images using Hybrid kMean-Graph Cut Technique , 2018 .

[40]  Daniel Cohen-Or,et al.  Co-segmentation for space-time co-located collections , 2017, The Visual Computer.

[41]  Arturo Garcia-Perez,et al.  Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation , 2017, Neurocomputing.

[42]  Pushmeet Kohli,et al.  Minimizing sparse higher order energy functions of discrete variables , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[43]  Ling Shao,et al.  Submodular Trajectories for Better Motion Segmentation in Videos , 2018, IEEE Transactions on Image Processing.

[44]  Gabriel Taubin,et al.  Automatic segmentation of point clouds from multi-view reconstruction using graph-cut , 2016, The Visual Computer.

[45]  Rüdiger Westermann,et al.  Random Walks for Interactive Organ Segmentation in Two and Three Dimensions: Implementation and Validation , 2005, MICCAI.

[46]  Ben Glocker,et al.  Modality Propagation: Coherent Synthesis of Subject-Specific Scans with Data-Driven Regularization , 2013, MICCAI.

[47]  Heng-Da Cheng,et al.  A novel automatic seed point selection algorithm for breast ultrasound images , 2008, 2008 19th International Conference on Pattern Recognition.

[48]  Antonio Criminisi,et al.  Segmentation of Brain Tumor Tissues with Convolutional Neural Networks , 2014 .

[49]  Dimitris N. Metaxas,et al.  Automatic boundary extraction of ultrasonic breast lesions , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[50]  Vladimir Kolmogorov,et al.  What metrics can be approximated by geo-cuts, or global optimization of length/area and flux , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[51]  Ling Shao,et al.  Video Salient Object Detection via Fully Convolutional Networks , 2017, IEEE Transactions on Image Processing.

[52]  Qiang Wang,et al.  Multiscale superpixel classification for tumor segmentation in breast ultrasound images , 2012, 2012 19th IEEE International Conference on Image Processing.

[53]  Qianjin Feng,et al.  D brain tumor segmentation in multimodal MR images based on earning population-and patient-specific feature sets , 2013 .

[54]  David Dagan Feng,et al.  Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation , 2017, The Visual Computer.

[55]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[56]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[57]  Bernhard Hemmer,et al.  An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.

[58]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[59]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[60]  Wenguan Wang,et al.  Deep Visual Attention Prediction , 2017, IEEE Transactions on Image Processing.

[61]  Nader Karimi,et al.  Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images , 2018, ArXiv.

[62]  Yuxuan Wang,et al.  Completely automated segmentation approach for breast ultrasound images using multiple-domain features. , 2012, Ultrasound in medicine & biology.

[63]  Qianjin Feng,et al.  Segmentation of ultrasonic breast tumors based on homogeneous patch. , 2012, Medical physics.

[64]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[65]  Nicholas Ayache,et al.  A Generative Model for Brain Tumor Segmentation in Multi-Modal Images , 2010, MICCAI.

[66]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[67]  Leo Grady,et al.  A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[68]  Bernd Hamann,et al.  Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering , 2019, The Visual Computer.

[69]  Yu Ding,et al.  Segmentation, Inference and Classification of Partially Overlapping Nanoparticles , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[70]  Yuhong Li,et al.  Brain tumor segmentation from multimodal magnetic resonance images via sparse representation , 2016, Artif. Intell. Medicine.

[71]  Xiao Qin,et al.  Image segmentation based on modified superpixel segmentation and spectral clustering , 2018, The Journal of Engineering.