Literature DB >> 10576711

Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: preliminary results.

O Yu1, Y Mauss, G Zollner, I J Namer, J Chambron.   

Abstract

The benefits of texture analysis of magnetic resonance images have been assessed in multiple sclerosis (MS) patients. Out of thirty-two lesions identified in eight MS patients, nine were considered active, judging from their gadolinium uptake. Texture analysis allowed to obtain forty-two characterizing parameters for each lesion. Using discriminant analysis as a statistical method allowed to classify the lesions into two groups: active or non-active. An attempt to classify their level of activity by using only co-occurrence matrices was unsuccessful. Alternately, the same type of analysis performed on runlength analysis criteria allowed the accurate classification of 88% of active lesions and 96% of non-active lesions. Using incremental discriminate analysis can reduce the number of useful parameters. This method showed that among the 42 parameters, 8 only were highly significant and permitted an accurate classification. Five of these parameters are runlength parameters, and three others are more directly related to the global distribution. The main interest of runlength parameters is that they allowed to demonstrate that the lesion structure was different in active and non-active plaques. This preliminary work suggests that using texture analysis could be of interest in the follow-up of MS patients because it provides an opportunity to identify active lesions without frequent gadolinium injections.

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Year:  1999        PMID: 10576711     DOI: 10.1016/s0730-725x(99)00062-4

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  8 in total

Review 1.  Texture analysis: a review of neurologic MR imaging applications.

Authors:  A Kassner; R E Thornhill
Journal:  AJNR Am J Neuroradiol       Date:  2010-04-15       Impact factor: 3.825

Review 2.  Image texture characterization using the discrete orthonormal S-transform.

Authors:  Sylvia Drabycz; Robert G Stockwell; J Ross Mitchell
Journal:  J Digit Imaging       Date:  2008-08-02       Impact factor: 4.056

3.  FLAIR signal and texture analysis for lateralizing mesial temporal lobe epilepsy.

Authors:  Kourosh Jafari-Khouzani; Kost Elisevich; Suresh Patel; Brien Smith; Hamid Soltanian-Zadeh
Journal:  Neuroimage       Date:  2009-09-08       Impact factor: 6.556

4.  MRI texture analysis in multiple sclerosis.

Authors:  Yunyan Zhang
Journal:  Int J Biomed Imaging       Date:  2011-11-16

5.  Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models.

Authors:  Ahmad Chaddad
Journal:  Int J Biomed Imaging       Date:  2015-06-02

6.  Texture Analysis of T2-Weighted MR Images to Assess Acute Inflammation in Brain MS Lesions.

Authors:  Nicolas Michoux; Alain Guillet; Denis Rommel; Giosué Mazzamuto; Christian Sindic; Thierry Duprez
Journal:  PLoS One       Date:  2015-12-22       Impact factor: 3.240

7.  Characterization of microcirculation in multiple sclerosis lesions by dynamic texture parameter analysis (DTPA).

Authors:  Rajeev Kumar Verma; Johannes Slotboom; Mirjam Rachel Heldner; Mirjam Rahel Heldner; Frauke Kellner-Weldon; Raimund Kottke; Christoph Ozdoba; Christian Weisstanner; Christian Philipp Kamm; Roland Wiest
Journal:  PLoS One       Date:  2013-07-16       Impact factor: 3.240

8.  Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging.

Authors:  Rajeev K Verma; Johannes Slotboom; Cäcilia Locher; Mirjam R Heldner; Christian Weisstanner; Eugenio Abela; Frauke Kellner-Weldon; Martin Zbinden; Christian P Kamm; Roland Wiest
Journal:  Biomed Res Int       Date:  2016-01-13       Impact factor: 3.411

  8 in total

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