Texture analysis of multiple sclerosis: a comparative study

Zhang, Jing; Tong, Longzheng; Wang, Lei; Li, Ning

doi:10.1016/j.mri.2008.01.016

Cited by 92 publications

(78 citation statements)

References 32 publications

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“…If measures are not taken to reduce the number of features before classification, then the statistical model will better reflect the noise or random error than the underlying data (so-called "overfitting"). 23 Fortunately, there are a number of strategies available for dimensionality reduction, beginning with simple consolidation or averaging of a given feature over all directions. 12 One can also manually select a subset of features a priori, particularly if previous work has convincingly isolated the features most relevant to the hypothesis being tested.…”

Section: Feature Selection and Extraction: Avoiding "Fishing Expeditimentioning

confidence: 99%

“…53 However, T2 hyperintensity is not particularly specific for MS and appears to be unsuitable for identifying incremental changes in NAWM with time. 54 Zhang et al 23 investigated Ͼ200 texture features extracted from the T2-weighted MR images of 16 patients with RRMS. The authors selected features on the basis of the greatest difference between tissue classes (ie, MS lesions versus normal WM, MS lesions versus NAWM, and normal WM versus NAWM).…”

Section: Msmentioning

confidence: 99%

“…In the original 4D texture analysis study, Woods et al 62 developed an ANN classifier and trained it to classify the DCE data into benign and malignant tissue classes. Apart from 4D texture, others have developed ANN 23,29 as well as probabilistic neural network classifiers 68 to isolate the most discriminating textural features. Although neural network approaches initially require a great deal of data for training and validation, once this stage has been completed satisfactorily, these procedures could be implemented in a clinically reasonable timeframe.…”

Section: Advanced Pattern-recognition Techniquesmentioning

confidence: 99%

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Texture Analysis: A Review of Neurologic MR Imaging Applications

Kassner¹,

Thornhill²

2010

AJNR Am J Neuroradiol

358

265

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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.ABBREVIATIONS: ABSV ϭ absolute gradient value; AIS ϭ acute ischemic stroke; ANN ϭ artificial neural network; CIS ϭ clinically isolated syndrome; d ϭ distance; DCE ϭ dynamic contrastenhanced; FCD ϭ focal cortical dysplasia; FLAIR ϭ fluid-attenuated inversion recovery; f x ϭ second-order gray-level co-occurrence feature number "x"; GLCM ϭ gray-level co-occurrence matrix; GM ϭ gray matter; HT ϭ hemorrhagic transformation; LDA ϭ linear discriminant analysis; MGL ϭ mean gray level; MGR ϭ mean gradient; MS ϭ multiple sclerosis; MTR ϭ magnetization transfer ratio; NAWM ϭ normal-appearing white matter; N g ϭ number of gray levels; PCA ϭ principal components analysis; PPMS ϭ primary-progressive MS; RLM ϭ run-length matrix; ROC ϭ receiver-operator characteristic; ROI ϭ region of interest; RRMS ϭ relapsing-remitting MS; rtPA ϭ recombinant tissue plasminogen activator; SPMS ϭ secondary-progressive MS; SVM ϭ support vector machine; ϭ direction; VGL ϭ variance of gray levels; VGR ϭ variance gradient; WM ϭ white matter

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Section: Feature Selection and Extraction: Avoiding "Fishing Expeditimentioning

confidence: 99%

Section: Msmentioning

confidence: 99%

Section: Advanced Pattern-recognition Techniquesmentioning

confidence: 99%

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Texture Analysis: A Review of Neurologic MR Imaging Applications

Kassner¹,

Thornhill²

2010

AJNR Am J Neuroradiol

358

265

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“…McLaren et al 15 used morphologic, co-occurrence, and Laws texture parameters in MR images for breast cancer diagnosis; Rachidi et al 16 assessed osteoporosis through TA (among other tools). Theocharakis et al 17 studied multiple sclerosis by using histogram, co-occurrence, and run-length matrixϪbased features extracted from fluid-attenuated inversion recovery MR images; Zhang et al 18 applied TA based on the polar Stockwell Transform to gadolinium-enhanced T2 MR images, also for the study of multiple sclerosis.…”

mentioning

confidence: 99%

MR Imaging Texture Analysis of the Corpus Callosum and Thalamus in Amnestic Mild Cognitive Impairment and Mild Alzheimer Disease

Oliveira¹,

Balthazar²,

D’Abreu³

et al. 2010

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE:TA is a branch of image processing that seeks to reduce image information by extracting texture descriptors from the image. TA of MR images of anatomic structures in mild AD and aMCI is not well-studied. Our objective was to attempt to find differences among patients with aMCI and mild AD and normal-aging subjects, by using TA applied to the MR images of the CC and the thalami of these groups of subjects.

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“…Lucia Dettori(Lucia D et al,2007) selected Mean, StaDev, Energy and Entropy to establish the prediction model. Principal component analysis，a very useful tool to deal with colinearity, has various applications in texture extraction and tumor recognition (Zhang J et al,2008). Mohamed Meselhy Eltoukhy (Mohamed ME et al,2010) used Curvelet transform to decompose mammogram images into 4 levels, then selected the largest 100 texture features as parameters.…”

Section: Examplementioning

confidence: 99%