Functional Magnetic Resonant Imaging (fMRI) is a non-invasive imaging technique used to study the brain. Neuroscientists have developed various algorithms to determine which voxels of the images are active. Most of these algorithms, operating on the signal of each voxel separately, are referred to as the voxel-by-voxel detectors. Among those voxel-by-voxel detectors, paired T-test and General L...

Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise ...

Since the introduction of cone-beam computed tomography (CBCT), several novel systems with different technical specifications and settings have become commercially available. Therefore, it is essential to evaluate CBCT systems for differences in the subjective quality of images obtained for various dental procedures. We evaluated the subjective image quality of cross-sectional scans obtained fr...

Functional Magnetic Resonance Imaging (fMRI) data consists of time series for each voxel recorded during a cognitive task. In order to extract useful information from this noisy and redundant data, techniques are proposed to select the voxels that are relevant to the underlying cognitive task. We propose a simple and efficient algorithm for decoding the brain states by modelling the correlation...

Point cloud classification is a key technology for point applications and feature extraction step towards achieving classification. Although there are many methods, the acquisition of colored data has become easier in recent years, most processing algorithms do not consider color information associated with or make full use information. Therefore, we propose voxel-based local descriptor accordi...