I saw a presentation last month that mentioned a user request to have the ability to customize regionprops. That is, a user wanted to be able to add their own measurement to regionprops.

Today, I'll show you how to do this yourself.

First, here's a brief recap on what regionprops does. The function computes measurements of image regions. Some of these measurements are based purely on a region's shape, while others incorporate pixel values within the regions. Here's an example using the coins.png sample image.

I = imread('coins.png');
imshow(I)

Let's convert this image to binary, using adaptive thresholding, filling holes, and removing small "noise" pixels.

bw = imbinarize(I,'adaptive');
bw = imfill(bw,'holes');
bw = bwareafilt(bw,[100 Inf]);
imshow(bw)

You can count the "blobs" (object) yourself; there are 10 of them.

The simplest regionprops call, regionprops(bw) computes the Area, Centroid, and BoundingBox for each object.

s = regionprops(bw)

s = 

  10×1 struct array with fields:

    Area
    Centroid
    BoundingBox

But I don't think this is the best way to call regionprops anymore. You can now tell regionprops to return the results as a table.

t = regionprops('table',bw)

t =

  10×3 table

    Area        Centroid        BoundingBox 
    ____    ________________    ____________

    2635    37.133    106.85    [1x4 double]
    1846    56.131    49.693    [1x4 double]
    2672    96.199    146.05    [1x4 double]
    1839    109.97    84.848    [1x4 double]
    2744    120.37    208.73    [1x4 double]
    2520    148.57    34.404    [1x4 double]
    2589    174.83    120.01    [1x4 double]
    2518    216.81    70.649    [1x4 double]
    1857    236.03    173.36    [1x4 double]
    1829    265.96    102.64    [1x4 double]

The table form is a lot more convenient for many tasks. For today's topic, one especially nice thing thing about tables is how easy it is to add your own variables to the table.

To illustrate, let's add a measurement that I've seen called Roundness. One definition for roundness is:

$R = \frac{4A}{\pi L^2}$

where $A$ is the object area and $L$ is the major axis length of the best-fit ellipse for the object. Here's how to compute roundness and add it directly to the measurements returned by regionprops.

First, note that both Area and MajorAxisLength are supported by regionprops, so let's start with those.

t = regionprops('table',bw,'Area','MajorAxisLength')

t =

  10×2 table

    Area    MajorAxisLength
    ____    _______________

    2635     60.08         
    1846    50.178         
    2672    59.792         
    1839    49.674         
    2744    60.374         
    2520     58.08         
    2589    58.676         
    2518    58.162         
    1857     49.77         
    1829    49.564         

You access table variables using dot notation, like t.Area. Similarly, you can create a new table variable using dot notation and assignment, like t.MyVariable = .... So adding Roundness to the table returned by regionprops is this simple.

t.Roundness = 4 * t.Area ./ (pi * t.MajorAxisLength.^2)

t =

  10×3 table

    Area    MajorAxisLength    Roundness
    ____    _______________    _________

    2635     60.08             0.92945  
    1846    50.178             0.93352  
    2672    59.792              0.9516  
    1839    49.674             0.94893  
    2744    60.374              0.9585  
    2520     58.08             0.95118  
    2589    58.676             0.95745  
    2518    58.162             0.94772  
    1857     49.77             0.95453  
    1829    49.564             0.94798  

Let's try this computation with an image containing objects that are not quite as round.

I2 = imread('rice.png');
imshow(I2)

bw2 = imbinarize(I2,'adaptive');
bw2 = imfill(bw2,'holes');
bw2 = bwareafilt(bw2,[100 Inf]);
imshow(bw2)

t2 = regionprops('table',bw2,'Area','MajorAxisLength');
t2.Roundness = 4 * t2.Area ./ (pi * t2.MajorAxisLength.^2);
head(t2)

ans =

  8×3 table

    Area    MajorAxisLength    Roundness
    ____    _______________    _________

    138     23.594             0.31562  
    120     18.152              0.4637  
    169     28.123             0.27207  
    157     23.793              0.3531  
    284     43.757             0.18885  
    200     26.259             0.36929  
    141     21.647             0.38311  
    177     29.087             0.26636  

I'm a big fan of the (relatively) new histogram function in MATLAB, so let's use it to compare our roundness numbers. I will follow the advice given in the histogram reference page for normalizing multiple histograms so that they can be more easily compared. I'll set the y-axis limits to [0 1], which is appropriate for probability normalization, and I'll set the x-axis limits to [0 1], which is the range for Roundness.

h1 = histogram(t.Roundness);
hold on
h2 = histogram(t2.Roundness);
hold off
h1.Normalization = 'probability';
h2.Normalization = 'probability';
h1.BinWidth = 0.02;
h2.BinWidth = 0.02;

xlim([0 1]);
ylim([0 1]);

title('Histogram of roundness (probability normalization)')
legend('coins','rice')

There you have it. You can add your own object measurements to the output of regionprops. It's especially easy if you tell regionprops to return a table.

I'll leave you with this question, dear reader: Are there measurements you would like us to add to regionprops? I am aware of an enhancement request for the Feret diameter. What else would you like to see?

Get the MATLAB code

Published with MATLAB® R2017a