White matter hyper-intensities automatic identification and segmentation in magnetic resonance images

Lizette Johanna Patino-Correa, Oleksiy Pogrebnyak, Jesus Alberto Martinez-Castro, Edgardo Manuel Felipe-Riveron

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

A methodology for automatic identification and segmentation of white matter hyper-intensities appearing in magnetic resonance images of brain axial cuts is presented. To this end, a sequence of image processing technics is employed to form an image where the hyper-intensities in white matter differ notoriously from the rest of the objects. This pre-processing stage facilitates the posterior process of identification and segmentation of the hyper-intensity volumes. The proposed methodology was tested on 55 magnetic resonance images from six patients. These images were analysed by the proposed system and the resulted hyper-intensity images were compared with the images manually segmented by experts. The experimental results show the mean rate of true positives of 0.9, the mean rate of false positives of 0.7 and the similarity index of 0.7; it is worth commenting that the false positives are found mostly within the grey matter not causing problems in early diagnosis. The proposed methodology for magnetic resonance image processing and analysis may be useful in the early detection of white matter lesions. © 2014 Elsevier Ltd. All rights reserved.
Original languageAmerican English
Pages (from-to)7114-7123
Number of pages6401
JournalExpert Systems with Applications
DOIs
StatePublished - 15 Nov 2014

Fingerprint

Magnetic resonance
Magnetic Resonance Spectroscopy
Image processing
Image analysis
Early Diagnosis
Brain
Processing
White Matter

Cite this

@article{1be2673f15d04ce28388febf775d304e,
title = "White matter hyper-intensities automatic identification and segmentation in magnetic resonance images",
abstract = "A methodology for automatic identification and segmentation of white matter hyper-intensities appearing in magnetic resonance images of brain axial cuts is presented. To this end, a sequence of image processing technics is employed to form an image where the hyper-intensities in white matter differ notoriously from the rest of the objects. This pre-processing stage facilitates the posterior process of identification and segmentation of the hyper-intensity volumes. The proposed methodology was tested on 55 magnetic resonance images from six patients. These images were analysed by the proposed system and the resulted hyper-intensity images were compared with the images manually segmented by experts. The experimental results show the mean rate of true positives of 0.9, the mean rate of false positives of 0.7 and the similarity index of 0.7; it is worth commenting that the false positives are found mostly within the grey matter not causing problems in early diagnosis. The proposed methodology for magnetic resonance image processing and analysis may be useful in the early detection of white matter lesions. {\circledC} 2014 Elsevier Ltd. All rights reserved.",
author = "Patino-Correa, {Lizette Johanna} and Oleksiy Pogrebnyak and Martinez-Castro, {Jesus Alberto} and Felipe-Riveron, {Edgardo Manuel}",
year = "2014",
month = "11",
day = "15",
doi = "10.1016/j.eswa.2014.05.036",
language = "American English",
pages = "7114--7123",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Ltd",

}

White matter hyper-intensities automatic identification and segmentation in magnetic resonance images. / Patino-Correa, Lizette Johanna; Pogrebnyak, Oleksiy; Martinez-Castro, Jesus Alberto; Felipe-Riveron, Edgardo Manuel.

In: Expert Systems with Applications, 15.11.2014, p. 7114-7123.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - White matter hyper-intensities automatic identification and segmentation in magnetic resonance images

AU - Patino-Correa, Lizette Johanna

AU - Pogrebnyak, Oleksiy

AU - Martinez-Castro, Jesus Alberto

AU - Felipe-Riveron, Edgardo Manuel

PY - 2014/11/15

Y1 - 2014/11/15

N2 - A methodology for automatic identification and segmentation of white matter hyper-intensities appearing in magnetic resonance images of brain axial cuts is presented. To this end, a sequence of image processing technics is employed to form an image where the hyper-intensities in white matter differ notoriously from the rest of the objects. This pre-processing stage facilitates the posterior process of identification and segmentation of the hyper-intensity volumes. The proposed methodology was tested on 55 magnetic resonance images from six patients. These images were analysed by the proposed system and the resulted hyper-intensity images were compared with the images manually segmented by experts. The experimental results show the mean rate of true positives of 0.9, the mean rate of false positives of 0.7 and the similarity index of 0.7; it is worth commenting that the false positives are found mostly within the grey matter not causing problems in early diagnosis. The proposed methodology for magnetic resonance image processing and analysis may be useful in the early detection of white matter lesions. © 2014 Elsevier Ltd. All rights reserved.

AB - A methodology for automatic identification and segmentation of white matter hyper-intensities appearing in magnetic resonance images of brain axial cuts is presented. To this end, a sequence of image processing technics is employed to form an image where the hyper-intensities in white matter differ notoriously from the rest of the objects. This pre-processing stage facilitates the posterior process of identification and segmentation of the hyper-intensity volumes. The proposed methodology was tested on 55 magnetic resonance images from six patients. These images were analysed by the proposed system and the resulted hyper-intensity images were compared with the images manually segmented by experts. The experimental results show the mean rate of true positives of 0.9, the mean rate of false positives of 0.7 and the similarity index of 0.7; it is worth commenting that the false positives are found mostly within the grey matter not causing problems in early diagnosis. The proposed methodology for magnetic resonance image processing and analysis may be useful in the early detection of white matter lesions. © 2014 Elsevier Ltd. All rights reserved.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84904308802&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84904308802&origin=inward

U2 - 10.1016/j.eswa.2014.05.036

DO - 10.1016/j.eswa.2014.05.036

M3 - Article

SP - 7114

EP - 7123

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -