We obtained T2 weighted and proton density weighted brain images using a two dimensional axial turbo spin echo sequence (repetition time 3500 ms, first echo time 15 ms, second echo time 85 ms) with 26 slices (each 5 mm thick).
Briefly, segmented labels of brain neuropils (AL, MBc, LH) were registered onto a reference brain image using affine registration followed by elastic warping.
We then aligned the high-resolution image tile to the low-resolution whole brain image, using the reference nc82 channel, by means of image stitching (Yu and Peng, 2011), which obtains translations through searching the maximum normalized cross correlation using the fast Fourier transform (FFT).
Thiyagarajan and Aghila [19] proposed a new steganography methodology for hiding patient information inside a brain cover image using a dynamic key produced by graph 3 coloring problem.
The most widely used method for the reconstruction of quantitative brain images using PET is filtered back projection, an analytical image reconstruction method.
To obtain a coarse alignment of the brain images using the harmonic signals only and the pixel-based registration of SHG/THG image sequences, we used the RecursiveReg Matlab script for Imaris provided by Michael Liebling (UCSB, California, USA) (Thévenaz et al., 1998) without allowing deformation ('rigid body' option).
An effective method for segmentation of MR brain images using the ant colony optimization algorithm.
Then, the polystyrene was spectrally mapped and identified in the exposed brain images using the Spectral Angle Mapper algorithm.
Joseph et al. [2] proposed segmentation of MRI brain images using K-means clustering algorithm along with morphological filtering for the detection of tumor images.
Data acquisition was performed according to the ENCDAT protocol (EANM Research Ltd. (EARL /European Network of Excellence for Brain Imaging) using the scanners and collimators specified in the ENCDAT protocol.
