Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Survey on brain tumor segmentation and feature extraction of MR images

Survey on brain tumor segmentation and feature extraction of MR images Brain tumor analysis plays an important role in medical imaging applications and in delivering a huge amount of anatomical and functional information, which increases and simplifies the diagnosis and disease therapy planning. However, the presence of image artifacts such as noise, intensity inhomogeneity and partial volume effect in magnetic resonance images can aggressively affect the quantitative brain tumor analysis. Also, the complex anatomy of the brain is another necessary factor to deal with. To avoid or reduce manual segmentation error, the automatic segmentation and detection of tumor have become the most challenging task for radiologists and clinicians. In this paper, most commonly used MR brain image segmentation algorithms and most popular brain MRI features are surveyed and summarized with an emphasis on their characteristics, merits, and demerits of these techniques. This paper presents a categorization of various segmentation algorithms ranging from simple threshold methods to high-level segmentation techniques such as deformable methods, graph-based, and deep learning approaches with a focus on gliomas which is most common of all malignant brain and central nervous system tumors. We also discuss the current trends with a focus on brain tumor segmentation, tissue segmentation and lesion detection using deep learning methods such as deep neural networks and convolutional neural networks. We also mentioned the future improvements to standardize the MRI-based brain tumor detection method for clinical use. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Survey on brain tumor segmentation and feature extraction of MR images

Download PDF
21 pages

Loading next page...
 
/lp/springer-journals/survey-on-brain-tumor-segmentation-and-feature-extraction-of-mr-images-RLa7oG1lPK
Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Database Management
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-018-0162-2
Publisher site
See Article on Publisher Site

Abstract

Brain tumor analysis plays an important role in medical imaging applications and in delivering a huge amount of anatomical and functional information, which increases and simplifies the diagnosis and disease therapy planning. However, the presence of image artifacts such as noise, intensity inhomogeneity and partial volume effect in magnetic resonance images can aggressively affect the quantitative brain tumor analysis. Also, the complex anatomy of the brain is another necessary factor to deal with. To avoid or reduce manual segmentation error, the automatic segmentation and detection of tumor have become the most challenging task for radiologists and clinicians. In this paper, most commonly used MR brain image segmentation algorithms and most popular brain MRI features are surveyed and summarized with an emphasis on their characteristics, merits, and demerits of these techniques. This paper presents a categorization of various segmentation algorithms ranging from simple threshold methods to high-level segmentation techniques such as deformable methods, graph-based, and deep learning approaches with a focus on gliomas which is most common of all malignant brain and central nervous system tumors. We also discuss the current trends with a focus on brain tumor segmentation, tissue segmentation and lesion detection using deep learning methods such as deep neural networks and convolutional neural networks. We also mentioned the future improvements to standardize the MRI-based brain tumor detection method for clinical use.

Journal

International Journal of Multimedia Information Retrieval – Springer Journals

Published: Dec 11, 2018

References

  • The 2016 World Health Organization classification of tumors of the central nervous system: a summary
    Louis, DN; Perry, A; Reifenberger, G; Deimling, A; Figarella-Branger, D; Cavenee, WK; Ohgaki, H; Wiestler, OD; Kleihues, P; Ellison, DW
  • Indian data on central nervous tumors: a summary of published work
    Dasgupta, A; Gupta, T; Jalali, R
  • Image formation by induced local interactions: examples employing nuclear magnetic resonance
    Lauterbur, P
  • Multi-planar image formation using NMR spin echoes
    Mansfield, P
  • Magnetic resonance imaging: principles, methods, and techniques
    Sprawls, P
  • The multimodal brain tumor image segmentation benchmark (BRATS)
    Menze, BH; Jakab, A; Bauer, S; Kalpathy-Cramer, J; Farahani, K; Kirby, J; Burren, Y; Porz, N; Slotboom, J; Wiest, R
  • Automatic segmentation and reconstruction of the cortex from neonatal MRI
    Xue, H; Srinivasan, L; Jiang, S; Rutherford, M; Edwards, AD; Rueckert, D; Hajnal, JV
  • Fast robust automated brain extraction
    Smith, SM
  • Enhanced brain extraction improves the accuracy of brain atrophy estimation
    Battaglini, M; Smith, SM; Brogi, S; Stefano, N
  • Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies
    Ortiz, A; Górriz, J; Ramírez, J; Salas-Gonzalez, D; Llamas-Elvira, JM
  • Central brightening due to constructive interference with, without, and despite dielectric resonance
    Collins, CM; Liu, W; Schreiber, W; Yang, QX; Smith, MB
  • A nonparametric method for automatic correction of intensity nonuniformity in MRI data
    Sled, JG; Zijdenbos, AP; Evans, AC
  • Rapid and effective correction of RF inhomogeneity for high field magnetic resonance imaging
    Cohen, MS; DuBois, RM; Zeineh, MM
  • Magnetic resonance image tissue classification using a partial volume model
    Shattuck, DW; Sandor-Leahy, SR; Schaper, KA; Rottenberg, DA; Leahy, RM
  • Correction of differential intensity inhomogeneity in longitudinal MR images
    Lewis, EB; Fox, NC
  • Digital image using Matlab processing
    Gonzalez, RC; Woods, RE; Eddins, SL
  • A survey of MRI-based medical image analysis for brain tumor studies
    Bauer, S; Wiest, R; Nolte, L-P; Reyes, M
  • Tumour volume determination from MR images by morphological segmentation
    Gibbs, P; Buckley, DL; Blackband, SJ; Horsman, A
  • Improved delineation of brain tumors: an automated method for segmentation based on pathologic changes of 1H-MRSI metabolites in gliomas
    Stadlbauer, A; Moser, E; Gruber, S; Buslei, R; Nimsky, C; Fahlbusch, R; Ganslandt, O
  • A survey of thresholding techniques
    Sahoo, PK; Soltani, S; Wong, AK
  • Image segmentation techniques
    Haralick, RM; Shapiro, LG
  • Region-growing segmentation of brain vessels: an atlas-based automatic approach
    Passat, N; Ronse, C; Baruthio, J; Armspach, J-P; Maillot, C; Jahn, C
  • A combined region growing and deformable model method for extraction of closed surfaces in 3D CT and MRI scans
    Fresno, M; Vénere, M; Clausse, A
  • Brain MR images segmentation using statistical ratio: mapping between watershed and competitive hopfield clustering network algorithms
    Kuo, W-F; Lin, C-Y; Sun, Y-N
  • Watershed-based segmentation and region merging
    Bleau, A; Leon, LJ
  • Multiparameter segmentation and quantization of brain tumor from MRI images
    Ratan, R; Sharma, S; Sharma, S
  • A clustering fusion technique for MR brain tissue segmentation
    Al-Dmour, H; Al-Ani, A
  • Image segmentation by clustering
    Coleman, GB; Andrews, HC
  • Fuzzy $$c$$ c -mean based brain MRI segmentation algorithms
    Balafar, M
  • A multiscale and multiblock fuzzy $$c$$ c -means classification method for brain MR images
    Yang, X; Fei, B
  • Current methods in medical image segmentation
    Pham, DL; Xu, C; Prince, JL
  • Segmentation of MRI using new unsupervised fuzzy C mean algorithm
    Kannan, S
  • Fast fuzzy clustering
    Cheng, TW; Goldgof, DB; Hall, LO
  • Efficient algorithm for channel-optimised vector quantisation
    Han, J-K; Kim, H-M
  • High performance data mining
    Xu, X; Jäger, J; Kriegel, H-P
  • Clustering multispectral images: a tutorial
    Tran, TN; Wehrens, R; Buydens, LM
  • Markov random field segmentation of brain MR images
    Held, K; Kops, ER; Krause, BJ; Wells, WM; Kikinis, R; Muller-Gartner, H-W
  • Adaptive segmentation of MRI data
    Wells, WM; Grimson, WEL; Kikinis, R; Jolesz, FA
  • Maximum likelihood estimation from uncertain data in the belief function framework
    Denoeux, T
  • Automated model-based tissue classification of MR images of the brain
    Leemput, K; Maes, F; Vandermeulen, D; Suetens, P
  • Hybrid genetic and variational expectation–maximization algorithm for Gaussian-mixture-model-based brain MR image segmentation
    Tian, G; Xia, Y; Zhang, Y; Feng, D
  • MR image segmentation using a power transformation approach
    Lee, J-D; Su, H-R; Cheng, PE; Liou, M; Aston, JA; Tsai, AC; Chen, C-Y
  • Parameter estimation for finite mixture distributions
    Davenport, J; Bezdek, J; Hathaway, R
  • Adaptive, template moderated, spatially varying statistical classification
    Warfield, SK; Kaus, M; Jolesz, FA; Kikinis, R
  • A fully automatic and robust brain MRI tissue classification method
    Cocosco, CA; Zijdenbos, AP; Evans, AC
  • MR image segmentation using MLM and artificial neural nets
    Clarke, L
  • Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study
    Ozkan, M; Dawant, BM; Maciunas, RJ
  • Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network
    Chaplot, S; Patnaik, L; Jagannathan, N
  • Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps
    Vijayakumar, C; Damayanti, G; Pant, R; Sreedhar, C
  • Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks
    Reddick, WE; Glass, JO; Cook, EN; Elkin, TD; Deaton, RJ
  • An improved implementation of brain tumor detection using segmentation based on neuro fuzzy technique 1
    Murugavalli, S; Rajamani, V
  • Unified segmentation
    Ashburner, J; Friston, KJ
  • Segmentation of brain MR images through a hidden markov random field model and the expectation–maximization algorithm
    Zhang, Y; Brady, M; Smith, S
  • Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
    Fischl, B; Salat, DH; Busa, E; Albert, M; Dieterich, M; Haselgrove, C; Kouwe, A; Killiany, R; Kennedy, D; Klaveness, S
  • A Bayesian model for joint segmentation and registration
    Pohl, KM; Fisher, J; Grimson, WEL; Kikinis, R; Wells, WM
  • Decision, estimation, and classification: an introduction to pattern recognition and related topics
    Therrien, CW; Therrien, CW
  • Fusion based glioma brain tumor detection and segmentation using ANFIS classification
    Selvapandian, A; Manivannan, K
  • Handbook of biomedical imaging
    Cuadra, MB; Duay, V; Thiran, J-P
  • Image segmentation using deformable models
    Xu, C; Pham, DL; Prince, JL
  • Cents: cortical enhanced neonatal tissue segmentation
    Shi, F; Shen, D; Yap, P-T; Fan, Y; Cheng, J-Z; An, H; Wald, LL; Gerig, G; Gilmore, JH; Lin, W
  • Automatic segmentation of newborn brain MRI
    Weisenfeld, NI; Warfield, SK
  • Automatic segmentation of mr images of the developing newborn brain
    Prastawa, M; Gilmore, JH; Lin, W; Gerig, G
  • A dynamic 4D probabilistic atlas of the developing brain
    Kuklisova-Murgasova, M; Aljabar, P; Srinivasan, L; Counsell, SJ; Doria, V; Serag, A; Gousias, IS; Boardman, JP; Rutherford, MA; Edwards, AD
  • Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI
    Zhou, Y; Bai, J
  • Automatic brain tumor segmentation by subject specific modification of atlas priors1
    Prastawa, M; Bullitt, E; Moon, N; Leemput, K; Gerig, G
  • Morphology-driven automatic segmentation of MR images of the neonatal brain
    Gui, L; Lisowski, R; Faundez, T; Hüppi, PS; Lazeyras, F; Kocher, M
  • Segmentation of neonatal brain MR images using patch-driven level sets
    Wang, L; Shi, F; Li, G; Gao, Y; Lin, W; Gilmore, JH; Shen, D
  • Snakes: active contour models
    Kass, M; Witkin, A; Terzopoulos, D
  • Constraints on deformable models: recovering 3D shape and nonrigid motion
    Terzopoulos, D; Witkin, AP; Kass, M
  • Finite-element methods for active contour models and balloons for 2-D and 3-D images
    Cohen, LD; Cohen, I
  • Active contours without edges
    Chan, TF; Vese, LA
  • Shape modeling with front propagation: a level set approach
    Malladi, R; Sethian, JA; Vemuri, BC
  • Geodesic active contours
    Caselles, V; Kimmel, R; Sapiro, G
  • A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI
    Li, C; Huang, R; Ding, Z; Gatenby, J; Metaxas, DN; Gore, JC
  • A hybrid geometric-statistical deformable model for automated 3-D segmentation in brain MRI
    Huang, A; Abugharbieh, R; Tam, R; Initiative, ADN
  • Geometric level set methods in imaging, vision, and graphics
    Kimmel, R
  • Integrated active contours for texture segmentation
    Sagiv, C; Sochen, NA; Zeevi, YY
  • Biomedical image segmentation using geometric deformable models and metaheuristics
    Mesejo, P; Valsecchi, A; Marrakchi-Kacem, L; Cagnoni, S; Damas, S
  • Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation
    Ilunga-Mbuyamba, E; Avina-Cervantes, JG; Garcia-Perez, A; Jesus, Romero-Troncoso R; Aguirre-Ramos, H; Cruz-Aceves, I; Chalopin, C
  • Glioma segmentation with a unified algorithm in multimodal MRI images
    Li, Q; Gao, Z; Wang, Q; Xia, J; Zhang, H; Zhang, H; Liu, H; Li, S
  • A multiphase level set framework for image segmentation using the Mumford and Shah model
    Vese, LA; Chan, TF
  • Completely convex formulation of the Chan–Vese image segmentation model
    Brown, ES; Chan, TF; Bresson, X
  • Existence and regularity of minimizers of a functional for unsupervised multiphase segmentation
    Kang, SH; March, R
  • Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation
    Wang, L; Li, C; Sun, Q; Xia, D; Kao, C-Y
  • A convex approach to minimal partitions
    Chambolle, A; Cremers, D; Pock, T
  • Global minimization for continuous multiphase partitioning problems using a dual approach
    Bae, E; Yuan, J; Tai, X-C
  • Brain anatomical structure segmentation by hybrid discriminative/generative models
    Tu, Z; Narr, KL; Dollár, P; Dinov, I; Thompson, PM; Toga, AW
  • Fast and robust multi-atlas segmentation of brain magnetic resonance images
    Lötjönen, JM; Wolz, R; Koikkalainen, JR; Thurfjell, L; Waldemar, G; Soininen, H; Rueckert, D; Initiative, ADN
  • Development of image-processing software for automatic segmentation of brain tumors in MR images
    Vijayakumar, C; Gharpure, DC
  • Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering
    Ortiz, A; Gorriz, J; Ramirez, J; Salas-Gonzalez, D
  • An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images
    Xue, J-H; Pizurica, A; Philips, W; Kerre, E; Walle, R; Lemahieu, I
  • Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation
    Li, BN; Chui, CK; Chang, S; Ong, SH
  • Segmentation of brain tissue from magnetic resonance images
    Kapur, T; Grimson, WEL; Wells, WM; Kikinis, R
  • Brain tumor segmentation with deep neural networks
    Havaei, M; Davy, A; Warde-Farley, D; Biard, A; Courville, A; Bengio, Y; Pal, C; Jodoin, P-M; Larochelle, H
  • Brain tumor segmentation using convolutional neural networks in MRI images
    Pereira, S; Pinto, A; Alves, V; Silva, CA
  • Textural features for image classification
    Haralick, RM; Shanmugam, K; Dinstein, I
  • Machine learning based brain tumour segmentation on limited data using local texture and abnormality
    Bonte, S; Goethals, I; Holen, R
  • Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features
    Nabizadeh, N; Kubat, M
  • Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
    Kamnitsas, K; Ledig, C; Newcombe, VF; Simpson, JP; Kane, AD; Menon, DK; Rueckert, D; Glocker, B
  • A survey of deep neural network architectures and their applications
    Liu, W; Wang, Z; Liu, X; Zeng, N; Liu, Y; Alsaadi, FE
  • Medical image analysis using convolutional neural networks: a review
    Anwar, SM; Majid, M; Qayyum, A; Awais, M; Alnowami, M; Khan, MK
  • Segmentation of glioma tumors using convolutional neural networks
    Anitha, R; Raja, DSS
  • Big data analysis for brain tumor detection: deep convolutional neural networks
    Amin, J; Sharif, M; Yasmin, M; Fernandes, SL
  • Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images
    Saouli, R; Akil, M; Kachouri, R