imagesc: how to set NaN as white color

I need to set NaN values as white color using imagesc. I attach the matrix which is a 1200x1200 matrix with different values equal to NaN.

Answer 1

[William Thielicke]

As this is still the first hit when searching on Google...

The best solution IMHO:

Setting data that is NaN to transparent

set(h, 'AlphaData', ~isnan(img_data))

https://stackoverflow.com/questions/14933039/matlab-imagesc-plotting-nan-values

[jon erickson]

agree - this is a really clever, quick, easy solution that works in vast majority of use-cases.

[Walter Roberson]

image objects do not (or historically did not) accept logical data, so you had to convert to double,

set(h, 'AlphaData', 1-isnan(img_data))

Answer 2

[Adam]

doc colormap

allows you to define whatever colourmap you want. You can tag a white point on the front of e.g. a Parula colourmap of any size you want, e.g.

cmap = [0 0 0; parula(6)];

In order to make sure only NaNs take the white value though you would likely have to do some messing around with caxis. It isn't easy to define a colourmap for many values where you want precisely one to have one specific colour on the colourmap, unless you map it to true RGB, in which case you can do whatever you want.

[jon erickson]

Great method - simple, easy, works. Also note that [1 1 1] will color Nan white, whereas [0 0 0] will color them black. Thanks for the solution!

[Sophia Salazar]

I just want to make sure I'm understanding how this works. So the built in matlab colormaps using imagesc will assign NaNs to the lowest value on the colormap. This replaces the lowest value on the clormap with white. My question is: if there are real values that are in the lowest value range, are those white as well or does this actually only get NaNs?

[Image Analyst]

Try it. My guess is that the bottom seventh of the pixel values (pixel values up to one seventh of the range, starting from the min up to the the min plus one seventh of the max-min range) will all show up as white.

[Sophia Salazar]

I think you're right. I ended up using jet(200) so that a smaller portion of the cmap is white, I think this works. Thanks!

[Adam]

Your data will be mapped linearly to your colourmap, so the bigger the colourmap the smaller the range of values mapped into each bin. If you have a 256-element colourmap then the bottom 1/256 ( 0.39% ) of values will be mapped to the first bin of the colourmap. Nans will also get mapped here, whatever size your colourmap and range your data.

This is why I said "It isn't easy to define a colourmap for many values where you want precisely one to have one specific colour on the colourmap". If you are dealing with a uint8 image it is quite easy as you can have 1 bin per pixel value and just add an extra bin for the NaNs.

As also mentioned, you can play around with the caxis limits to achieve the right effect, but it all depends on your data.

e.g.

a = rand( 10 );
a(3,4) = NaN;
figure; imagesc( a );
colourmap( [0 0 0; parula(256)] )

If you do only that then quite likely you will have multiple black pixels instead of just 1 (this is random data though so it depends on the particular randon numbers you get. In my run I ended up with 4 black squares)

Now you can try:

caxis( [-0.001 1] )

and some or all of the non-NaN values will now take on a colour from the true colourmap instead of white. But if you have a very small value you may need to go for more like

caxis( [-0.01 1] )

or

caxis( [-0.1 1] )

by trial and error (or it can be mathematically calculated, I just couldn't be bothered)

Answer 3

[Image Analyst]

Have you tried this solution:

yourImage(isnan(yourImage)) = 255;

Answer 4

[Emmanuel Atoleya Atindama]

First, you need to know the scale value of the color. Example white = 1, black = 0.

p = data_array(:,:,:);

p(isnan(data_array))=1; % this assigns white to all the nan values, while all other values maintain their original color

figure; imshow(p)

Say you want to assign white to all nan values and black to the known values.

p = zeros(size(data_array));

p(isnan(data_array))=1; % this assigns white to all the nan values, and black to all other values

figure; imshow(p)