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As an essential step in brain studies, measuring the distribution of major brain tissues, including gray matter, white matter and cerebrospinal fluid (CSF), using magnetic resonance imaging (MRI) has attracted extensive research efforts over the past years. Many brain tissue differentiation methods resulted from these efforts are based on the finite statistical mixture model, which however, in spite...
The increasing prevalence of dual medical imaging modalities, such as PET-CT scanners, poses both challenges and opportunities to image segmentation, as they provide distinct but complementary information. In this paper, we propose a novel segmentation algorithm for 3D brain PET-CT images, which classifies each voxel by fusing the voxel's memberships estimated from four points of view using the PET...
This paper proposed a general image segmentation model, namely the energy-minimization based image segmentation (EMBIS) model. This model converts image segmentation into a controlled optimization process minimizing the weighted sum of the feature energy and spatial energy, which interpret the homogeneity restriction and spatial constraints, respectively. The EMBIS model provides a unified understanding...
When dealing with high-dimensional datasets with fewer samples, feature selection and ensemble learning are two effective strategies. In this paper, we focus our attention on genetic algorithm based feature selection for ensemble learning. We use an improved GA algorithm (IGA) to reduce the dimensionality of the feature space, and then evaluate using bagging and Ada-Boost constructed by the reduced...
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