Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast k-NN classification for multichannel image data
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multidimensional segmentation evaluation for medical image data
Computer Methods and Programs in Biomedicine
IEEE Transactions on Image Processing
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For over last three decades, numerous automatic brain segmentation techniques in magnetic resonance (MR) images have been proposed. These techniques, however, need to be validated comprehensively. In this study, FS+LDDMM, a recently proposed fully automatic template-based brain segmentation technique, is validated. The validation method uses novel approach in which dependency of FS+LDDMM on initial segmentation parameters is evaluated. These segmentation parameters include choice of template, gross alignment, cropping size and initialization schemes. A database of 46 MR images from young ADHD subjects of an average age of 10.6 years is employed to segment caudate nucleus in subcortical region. The accuracy of the segmentation is computed by comparing FS+LDDMM segmentation with gold standard manual segmentation using metrics, such as, percent volume error, dice coefficient, L1 error and intraclass correlation coefficient (ICC). The FS+LDDMM shows robustness to all these parameters and outperforms FreeSurfer (FS) segmentation. To generalize the performance of FS+LDDMM, however, more experiments need to be conducted for various subcortical objects.