![]() ![]() ![]() This can be useful in certain types of analysis, such as if we want to isolate only grey matter, or only white matter, from the rest of the brain. We can use an image histogram like this to create a mask that isolates a particular range of intensity values in the image, while setting all other intensity values to zero. The peaks just above 10 and just below 20 reflect the concentration of similar intensity values corresponding to grey and white matter respectively. We can see a peak just above 10 on the x axis (note that the numbers on the x axis are bin numbers, not intensity values), with a slight decrease and then a second small peak just before 20, followed by a flat area. In the histogram above, there is a large peak close to zero which represents the fact that a large number of voxels in the image don’t contain the head at all, and therefore have values at or close to zero. This function requires several arguments, including the minimum and maximum intensity values that define the range of the x axis of the histogram, and the number of bins: We use this rather than the NumPy histogram function, because ndimage’s function is designed to work with 3D images. histogram() function to plot a histogram of our brain volume. Instead, as we can see above, there are many very dark voxels (outside of the head, and in some of the fluid-filled spaces inside the head), and then clusters of voxels that are darker grey (the grey matter, largely in the cerebral cortex that forms the outer layer of the brain), lighter grey (the white matter that comprises much of the inside of the brain), and also some very bright areas that are primarily due to areas of fat concentration. These can be informative because the distribution of intensity values in an anatomical image is not uniform. Histograms of the anatomical images show the number of voxels of a given intensity value. Larger values appear as brighter (whiter), and lower values appear as darker. This is determined by computing how many slices in the volume are not covered by our 16 subplots (i.e., the slices at either edge of the volume), and dividing this by 2, since half of those slices should be at one edge of the volume, at the other at the other end of the volume.Īs we saw above, the image is stored as a NumPy array, in which each voxel in the image is represented as a number, which is mapped to an intensity value in the colourmap when plotting. Finally, we compute a start_stop value which tells us which slice to start from (i.e., the first slice to plot), such that the set of slices we generate will be centered on the volume. Then, we determine the number of slices that will be covered by 16 subplots, spaced 11 slices apart from each other (which will be < 184). ![]() Instead, we use floor division ( //) to generate the integer result of dividing the number of slices by subplots ( 11) so that we get a step size that ensures we have 16 evenly-spaced slices through the volume. For this reason, we can’t simply run a for loop over a range of slice numbers that starts at 0 and goes up to the number of slices, in steps of n_slices / n_subplots. Our number of slices - 184 - does not divide evenly by 16 ( 184 / 16 = 11.5). For instance, below we will generate a 4 x 4 array of 16 subplots. The biggest trick with this is deciding on the number of subplots (slices) we want, and then doing the necessary math to select the appropriate slices from the 3D volume such that the slices are evenly-spaced through the volume, and centered in the middle of the volume. We can use our skills with Matplotlib subplots to plot a series of slices through the brain, which is a more comprehensive way of visualizing the data. Plotting a series of slices through a volume # Although the image is stored as an object of type, the object is designed in a way that allows us to treat it in many ways like a simple NumPy array, so standard properties like. We can view its x and y dimensions with the. ('ImageOrientationPatient', (-0.0, 1.0, 0.0, -0.0, -0.0, -1.0)),ī'Data converted to numpy array, raw data removed to preserve memory'),īecause we loaded in a single image slice, our image is 2-dimensional, like a photograph. ('InstitutionName', 'IWK Health Centre'), Averaging ERPs: Creating MNE Evoked objects.Working with Multielectrode Data in pandas.Effects of Light Intensity on Spike Rate.Basic Statistics in Python: t tests with SciPy.Accessibility and Human Factors in Plotting.Procedural versus Object-Oriented Plotting in Matplotlib. ![]()
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