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Evolutionary Intelligence

, Volume 11, Issue 1–2, pp 19–30 | Cite as

Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review

  • K. Michael Mahesh
  • J. Arokia Renjit
Special Issue
  • 88 Downloads

Abstract

In medical image analysis, brain tumor recognition through medical resonance images (MRIs) is a challenging task because of the complex structure of the brain and high diversity in appearance of tumor tissues. Hence, the need for efficient and objective tumor recognition technique is increasing, for clinical acceptance as well as routine clinical application. Proper brain tumor recognition provides anatomical information of abnormal tissues in the brain, which helps the doctor in planning treatment. The literature presents various techniques for brain tumor recognition. This review article aims to provide a comprehensive survey of MRI based brain tumor recognition techniques based on evolutional intelligence and segmentation. Accordingly, various research papers related to brain tumor recognition are reviewed, and survey taxonomy is presented centered on segmentation and classification based tumor recognition techniques. Based on the review, the analysis is provided based on feature extraction techniques, image datasets, implementation tools, evaluation measures and results. Finally, we present various research issues which are useful for researchers to further research in brain tumor recognition techniques.

Keywords

MRI Imaging modalities Brain tumor Classification Segmentation 

Abbreviations

MRI

Medical resonance image

CT

Computed tomography

MRS

MRSpectroscopy

DTI

Diffusion tensor imaging

EPI

Echo-planar imaging

WHO

World Health Organization

CSF

Cerebro spinal fluid

PD

Proton density

MS

Multiple sclerosis

MCSS

Multi-population Cuckoo search strategy

CS

Cuckoo search

MSFL

Modified shuffled frog leaping

FCM

Fuzzy C-means

ABC

Artificial bee colony

GM

Gray matter

WM

White matter

ANN

Artificial neural network

NN

Neural network

SOM

Self-organizing map

k-NN

k-nearest neighbor

SVM

Support vector machine

DNN

Deep neural network

FCN

Fully convolutional networks

RF

Random forest

GARCH

Generalized autoregressive conditional heteroscedasticity

FLAIR

Fluid-attenuated inversion recovery

ERT

Extremely randomized trees

MET

Metastasis

GBM

Glioblastoma multiforme

KFCM

k-means and fuzzy c-means

KPSO

k-means and particle swarm optimization

GLRLM

Gray level run length matrix

DWT

Discrete wavelet transformation

PCA

Principal component analysis

MICCAI

Medical image computing and computer assisted interventions

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.St. Joseph College of EngineeringChennaiIndia
  2. 2.Department of CSEJeppiaar Engineering CollegeChennaiIndia

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