Title of article :
Computer-assisted enhanced volumetric segmentation magnetic resonance imaging data using a mixture of artificial neural networks
Author/Authors :
Pérez de Alejo، نويسنده , , Rigoberto and Ruiz-Cabello، نويسنده , , Jesْs and Cortijo، نويسنده , , Manuel and Rodriguez، نويسنده , , Ignacio and Echave، نويسنده , , Imanol and Regadera، نويسنده , , Javier and Arrazola، نويسنده , , Juan and Avilés، نويسنده , , Pablo and Barreiro، نويسنده , , Pilar and Gargallo، نويسنده , , Domingo and Graٌa، نويسنده , , Manuel، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2003
Pages :
12
From page :
901
To page :
912
Abstract :
An accurate computer-assisted method able to perform regional segmentation on 3D single modality images and measure its volume is designed using a mixture of unsupervised and supervised artificial neural networks. Firstly, an unsupervised artificial neural network is used to estimate representative textures that appear in the images. The region of interest of the resultant images is selected by means of a multi-layer perceptron after a training using a single sample slice, which contains a central portion of the 3D region of interest. thod was applied to magnetic resonance imaging data collected from an experimental acute inflammatory model (T2 weighted) and from a clinical study of human Alzheimer’s disease (T1 weighted) to evaluate the proposed method. In the first case, a high correlation and parallelism was registered between the volumetric measurements, of the injured and healthy tissue, by the proposed method with respect to the manual measurements (r = 0.82 and p < 0.05) and to the histopathological studies (r = 0.87 and p < 0.05). The method was also applied to the clinical studies, and similar results were derived of the manual and semi–automatic volumetric measurement of both hippocampus and the corpus callosum (0.95 and 0.88).
Journal title :
Magnetic Resonance Imaging
Serial Year :
2003
Journal title :
Magnetic Resonance Imaging
Record number :
1831685
Link To Document :
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