Detrended fluctuation analysis of brain hemisphere magnetic resonnance images to detect cerebral arteriovenous malformations

Salim Lahmiri, Mounir Boukadoum, Antonio Di Ieva

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

Abstract

We present a fractal-based methodology to analyze brain magnetic resonance images (MRI) for the automated detection of cerebral arteriovenous malformations (AVM). First, the MRI is split into right and left hemispheres components whose fractal dimensions (FD) are estimated using detrended fluctuation analysis (DFA). Then, the obtained FD values are used to characterize healthy and AVM-affected brain MRIs. Using a database of twenty-eight images, and ten-fold cross validation, classification by a support vector machine (SVM) was 100% accurate when using either a linear or a radial basis Gaussian kernel, and the total image processing time was 32.75 s on a midrange PC station. It is concluded that the presented cerebral AVM detection system is both simple and accurate, and its processing time makes it compatible for use in a clinical environment, should it performance be confirmed with a larger image database.

LanguageEnglish
Title of host publicationIEEE International Symposium on Circuits and Systems : ISCAS 2014
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2409-2412
Number of pages4
ISBN (Electronic)9781479934324, 9781479934317
ISBN (Print)9781479934331
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 - Melbourne, VIC, Australia
Duration: 1 Jun 20145 Jun 2014

Other

Other2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014
CountryAustralia
CityMelbourne, VIC
Period1/06/145/06/14

Fingerprint

Fractal dimension
Magnetic resonance
Brain
Fractals
Magnetic resonance imaging
Support vector machines
Image processing
Processing

Cite this

Lahmiri, S., Boukadoum, M., & Di Ieva, A. (2014). Detrended fluctuation analysis of brain hemisphere magnetic resonnance images to detect cerebral arteriovenous malformations. In IEEE International Symposium on Circuits and Systems : ISCAS 2014: proceedings (pp. 2409-2412). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ISCAS.2014.6865658
Lahmiri, Salim ; Boukadoum, Mounir ; Di Ieva, Antonio. / Detrended fluctuation analysis of brain hemisphere magnetic resonnance images to detect cerebral arteriovenous malformations. IEEE International Symposium on Circuits and Systems : ISCAS 2014: proceedings. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2014. pp. 2409-2412
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Lahmiri, S, Boukadoum, M & Di Ieva, A 2014, Detrended fluctuation analysis of brain hemisphere magnetic resonnance images to detect cerebral arteriovenous malformations. in IEEE International Symposium on Circuits and Systems : ISCAS 2014: proceedings. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 2409-2412, 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014, Melbourne, VIC, Australia, 1/06/14. https://doi.org/10.1109/ISCAS.2014.6865658

Detrended fluctuation analysis of brain hemisphere magnetic resonnance images to detect cerebral arteriovenous malformations. / Lahmiri, Salim; Boukadoum, Mounir; Di Ieva, Antonio.

IEEE International Symposium on Circuits and Systems : ISCAS 2014: proceedings. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2014. p. 2409-2412.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

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Lahmiri S, Boukadoum M, Di Ieva A. Detrended fluctuation analysis of brain hemisphere magnetic resonnance images to detect cerebral arteriovenous malformations. In IEEE International Symposium on Circuits and Systems : ISCAS 2014: proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2014. p. 2409-2412 https://doi.org/10.1109/ISCAS.2014.6865658