污污草莓视频

There are numerous scripts used to automatically linearly and nonlinearly register one MINC volume to another.

• Linear Registration

, mritoself, and other tools with additional documentation in .

• Nonlinear Registration

bestnonlinreg.pl is the most commonly used tool.

• Monkey versions

monkey_best_lin_reg.pl and monkey_mritotal.pl are the scripts used to linearly and nonlinearly register monkeys to a monkey atlas such as the MNI Macaque Atlas.

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer鈥檚 Disease Neuroimaging Initiative databases.

In testing, a mean Dice similarity coefficient of 0.9834卤0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781卤0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.

Read more: downloads and tutorial.

A new Collaborative Approach for eNhanced Denoising under Low-light Excitation (CANDLE) is introduced for the processing of 3D laser scanning multiphoton microscopy images. CANDLE is designed to be robust for low signal-to-noise ratio (SNR) conditions typically encountered when imaging deep in scattering biological specimens. Based on an optimized non-local means filter involving the comparison of filtered patches, CANDLE locally adapts the amount of smoothing in order to deal with the noise inhomogeneity inherent to laser scanning fluorescence microscopy images. An extensive validation on synthetic data, images acquired on microspheres and in vivo images is presented. These experiments show that the CANDLE filter obtained competitive results compared to a state-of-the-art method and a locally adaptive optimized nonlocal means filter, especially under low SNR conditions (PSNR<8dB). Finally, the deeper imaging capabilities enabled by the proposed filter are demonstrated on deep tissue in vivo images of neurons and fine axonal processes in the Xenopus tadpole brain.

P. Coup茅, M. Munz, J. V. Manjon, E. Ruthazer, D. L. Collins.
, accepted in Medical Image Analysis, 2012.
(This is the author鈥檚 version of the paper, full reference coming soon.)

The Matlab software with documentation and example images is available for download here: