PT  - JOURNAL ARTICLE
AU  - Kassner, A.
AU  - Thornhill, R.E.
TI  - Texture Analysis: A Review of Neurologic MR Imaging Applications
AID  - 10.3174/ajnr.A2061
DP  - 2010 May 01
TA  - American Journal of Neuroradiology
PG  - 809--816
VI  - 31
IP  - 5
4099  - http://www.ajnr.org/content/31/5/809.short
4100  - http://www.ajnr.org/content/31/5/809.full
SO  - Am. J. Neuroradiol.2010 May 01; 31
AB  - SUMMARY: Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS. ABSVabsolute gradient valueAISacute ischemic strokeANNartificial neural networkCISclinically isolated syndromeddistanceDCEdynamic contrast-enhancedFCDfocal cortical dysplasiaFLAIRfluid-attenuated inversion recoveryfxsecond-order gray-level co-occurrence feature number “x”GLCMgray-level co-occurrence matrixGMgray matterHThemorrhagic transformationLDAlinear discriminant analysisMGLmean gray levelMGRmean gradientMSmultiple sclerosisMTRmagnetization transfer ratioNAWMnormal-appearing white matterNgnumber of gray levelsPCAprincipal components analysisPPMSprimary-progressive MSRLMrun-length matrixROCreceiver-operator characteristicROIregion of interestRRMSrelapsing-remitting MSrtPArecombinant tissue plasminogen activatorSPMSsecondary-progressive MSSVMsupport vector machineθdirectionVGLvariance of gray levelsVGRvariance gradientWMwhite matter