Coupling of radial-basis network and active contour model for multispectral brain MRI segmentation.
Magnetic resonance (MR) has been accepted as the reference image study in the clinical environment. The development of new sequences has allowed obtaining diverse images with high clinical importance and whose interpretation requires their joint analysis (multispectral MRI). Recent approaches to segment MRI point toward the definition of hybrid models, where the advantages of region and contour-based methods can be exploited to look for the integration or fusion of information, thus enhancing the performance of the individual approaches. Following this perspective, a hybrid model for multispectral brain MRI segmentation is presented. The model couples a segmenter, based on a radial basis network (RBFNNcc), and an active contour model, based on a cubic spline active contour (CSAC) interpolation. Both static and dynamic coupling of RBFNNcc and CSAC are proposed; the RBFNNcc stage provides an initial contour to the CSAC; the initial contour is optimally sampled with respect to its curvature variations; multispectral information and a restriction term are included into the CSAC energy equation. Segmentations were compared to a reference stack, indicating high-quality performance as measured by Tanimoto indexes of 0.74 +/- 0.08.
Neuroimaging Laboratory, Department of Electrical Engineering, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco #186, Col. Vicentina, México, DF 09340, Mexico. ravc@xanum.uam.mx
Raquel
R
Valdés-Cristerna
Verónica
V
Medina-Bañuelos
Oscar
O
Yáñez-Suárez
D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods*; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity