First, we would like to make sure we have some data to work with. The data is not located directly in the installed package to make it lightweight and some other repository restrictions.
To download the test data, we will use the WhiteStripe function
##  TRUE
Once the data is downloaded, we can access the files using the
We will focus on the T1-weighted image here:
## oro.nifti 0.11.4
= files[grep("T1", basename(files))] t1 = readNIfTI(fname = t1, reorient = FALSE)img
Here we will display the data in 3-dimensions and note that it is a skull-stripped image:
= img[img > 0] vals hist(vals, breaks = 2000)
Here we see the distribution of non-zero values. We use
2000 breaks, as this is the default in
whitestripe will use the last mode (intensity around
85) in this data.
Since the image is skull stripped, we will set
stripped = TRUE in the
= whitestripe(img = img, type = "T1", stripped = TRUE)ws
## Making Image VOI
## Making T1 Histogram
## Getting T1 Modes
## Smoothing Histogram
## Smoothing Derivative
## Quantile T1 VOI
##  "whitestripe.ind" "img.mode" "mask.img" ##  "mu.whitestripe" "sig.whitestripe" "img.mode.q" ##  "whitestripe" "whitestripe.width" "whitestripe.width.l" ##  "whitestripe.width.u" "err"
We see the progress points and the names of the output. This returns
the indices of the whitestripe and a
which is used to normalize the image:
= whitestripe_norm(img = img, indices = ws$whitestripe.ind)norm
We can visualize the mode selected and the white stripe:
hist(vals, breaks = 2000) abline(v = ws$mu.whitestripe, col = "blue") abline(v = ws$whitestripe, col = "red")
Here we see the blue line for the mode and the red lines for the voxel intensities within the white stripe.
We can also overlay the mask used for the white stripe:
= ws$mask.img mask == 0] = NA mask[mask orthographic(x = img, y = mask, col.y = "red")
We see white matter selected, but the fact that more values within
the posterior compared to the anterior part of the brain may indicate a
inhomogeneity correction needs to be applied before running
= norm[img > 0] norm_vals hist(norm_vals, breaks = 2000)
Here is a brief overview of the functionality of the WhiteStripe package. Additional information requests are welcome to the bug report/issue page.