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
N1 - Funding Information:
This work was supported by Instituto Politecnico National through the project SIP20131302. Authors express their appreciation to Instituto Nacional de Rehabilitación for the provided MRI data.
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.
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.
KW - Image segmentation
KW - Magnetic resonance image
KW - White matter hyper-intensities
UR - http://www.scopus.com/inward/record.url?scp=84904308802&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2014.05.036
DO - 10.1016/j.eswa.2014.05.036
M3 - Artículo
SN - 0957-4174
VL - 41
SP - 7114
EP - 7123
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 16
ER -