Clarified documentation

README.md

@@ -41,8 +41,20 @@ At the end of the processing, you can review:
   |9584   |17.143453301063012|21.092693041826486|1.1247654335215769|
   |9418   |19.558182966645223|19.61176661536486 |1.1093208813636863|
 
+## Description of the analysis
 
-## Description of the scripts
+Two NIfTI files are required: an initial scan and a re-scan without repositioning. The analysis script `process_data.sh`
+includes the following steps:
+
+- Check if a mask for the spinal cord and/or gray matter already exists. If not, segment them automatically.
+- Register the second scan to the first one. Use nearest-neighbour interpolation to preserve noise properties.
+- Compute white matter mask by subtracting the spinal cord and the gray matter masks. 
+- Compute `SNR_diff` using the two-image subtraction method (Dietrich et al. J Magn Reson Imaging, 2007).
+- Compute `SNR_mult` using the first scan (Griffanti et al., Biomed Sign Proc and Control, 2012).
+- Compute `Contrast` by dividing the mean signal in the GM by that in the WM, on a slice-by-slice basis and then 
+  average across slices.
+
+## Simulations
 
 * [simu_create_phantom.py](./simu_create_phantom.py): Generate synthetic phantom
 of WM and GM that can be used to validate the proposed evaluation metrics. The phantoms are generated with random noise,
@@ -55,30 +67,7 @@ This script is particularly useful for processing the large amount of files
 generated by the phantom construction.
 * [simu_make_figures.py](./simu_make_figures.py): Make figures to assess
 metrics sensitivity to image quality. Run after simu_process_data.py
-* [make_figures_compare_SNR.py](./make_figures_compare_SNR.py): Make figures to assess
-the correlation between SNR_single and SNR_diff.
-
-## Analysis
-
-Two NIfTI files are required: an initial scan and a re-scan without repositioning.
-
-### Pre-processing
-- The second image is registered to the first in order to compute the SNR using the two-image subtraction method.
-- The spinal cord and gray matter of each image are segmented automatically.
-- White matter segmentation is generated by subtracting the gray matter segmentation from the cord segmentation.
-
-### Signal-to-noise ratio (SNR):
-The SNR is determined with two different methods: SNR_diff and SNR_single.
-- SNR_diff is computed with SCT using the two-image subtraction method (Dietrich et al. J Magn Reson Imaging, 2007).
-- SNR_single is computed from a single image (Griffanti et al., Biomed Sign Proc and Control, 2012).
-
-### Contrast:
-The mean signal is computed in the white matter and gray matter of image 1. The contrast is then computed according to the following equation:
-
-~~~
-Contrast = abs(mean(WM) - mean(GM)) / min{mean(WM),mean(GM)}
-~~~
-
+
 ## Configuration of Niftyweb server
 - make sure the script WMGM is declared in `PATH`
 - add an entry to the crontab that points to the Daemon. Example (to edit, use `crontab -e`):