9/22/13 Getting Started (Image Processing Toolbox) radio.feld.cvut.cz/matlab/toolbox/images/getting3.html 1/15 Image Processing Toolbox Exercise 2 -- Advanced Topics In this exercise you will work with another intensity image, rice.tif and explore some more advanced operations. The goals of this exercise are to remove the nonuniform background from rice.tif , convert the resulting image to a binary image by using thresholding, use components labeling to return the number of objects (grains or partial grains) in the image, and compute feature statistics. 1. Read and Display An Image Clear the MATLAB workspace of any variables and close open figure windows. Read and display the intensity image rice.tif . clear, close all I = imread('rice.tif'); imshow(I) 2. Perform Block Processing to Approximate the Background Notice that the background illumination is brighter in the center of the image than at the bottom. Use the blkproc function to find a coarse estimate of the background illumination by finding the minimum pixel value of each 32-by-32 block in the image. backApprox = blkproc(I,[32 32],'min(x(:))'); To see what was returned to backApprox , type backApprox MATLAB responds with
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9/22/13 Getting Started (Image Processing Toolbox)
Step 1. You used the toolbox functions imread and imshow to read and display an 8-bit intensity
image. imread and imshow are discussed in Exercise 1, in 2. Check the Image in Memory, under the
"Here's What Just Happened" discussion.
Step 2. blkproc found the minimum value of each 32-by-32 block of I and returned an 8-by-8
matrix, backApprox. You called blkproc with an input image of I, and a vector of [32 32], which
means that I will be divided into 32-by-32 blocks. blkproc is an example of a "function function,"meaning that it enables you to supply your own function as an input argument. You can pass in the name
of an M-file, the variable name of an inline function, or a string containing an expression (this is themethod that you used above). The function defined in the string argument ('min(x(:))') tells
blkproc what operation to perform on each block. For detailed instructions on using functionfunctions, see Appendix A.
MATLAB's min function returns the minimum value of each column of the array within parentheses. Toget the minimum value of the entire block, use the notation (x(:)), which reshapes the entire block intoa single column. For more information, see min in the MATLAB Function Reference.
3. Display the Background Approximation As a Surface
Use the surf command to create a surface display of the background approximation, backApprox. surf
requires data of class double, however, so you first need to convert backApprox using the double command.You also need to divide the converted data by 255 to bring the pixel values into the proper range for an image ofclass double, [0 1].
backApprox = double(backApprox)/255; % Convert image to double.figure, surf(backApprox);
rows of the image. The lowest pixel values occur at the bottom of the image and are represented in the surface
plot by the lowest part of the curve. Because the minimum intensity values in each block of this image make asmooth transition across the image, the surface is comprised of fairly smooth curves.
The surface plot is a Handle Graphics® object, and you can therefore fine-tune its appearance by setting
properties. ("Handle Graphics" is the name for the collection of low-level graphics commands that create theobjects you generate using MATLAB.) The call to reverse the y-axis is one of many property settings that youcan make. It was made using the set command, which is used to set all properties. In the line,
set(gca,'ydir','reverse');
gca refers to the handle of the current axes object and stands for "get current axes." You can also set manyproperties through the Property Editor. To invoke the Property Editor, open the figure window's Edit
menu, and select Figure Properties, Axes Properties, or Current Object Properties. To select an object
to modify with the Property Editor, click the property the following button on the figure window, , then
click on the object. You can also use the other buttons in the toolbar to add new text or line objects to your
figure.
For information on working with MATLAB graphics, see the MATLAB graphics documentation.
4. Resize the Background Approximation
Our estimate of the background illumination is only 8-by-8. Expand the background to the same size as the
original background image (256-by-256) by using the imresize function, then display it.
Step 4. You used imresize with "bilinear" interpolation to resize your 8-by-8 background
approximation, backApprox, to an image of size 256-by-256, so that it is now the same size as
rice.tif. If you compare it to rice.tif you can see that it is a very good approximation. The goodapproximation is possible because of the low spatial frequency of the background. A high-frequency
background, such as a field of grass, could not have been as accurately approximated using so few
blocks.
The interpolation method that you choose for imresize determines the values for the new pixels you
add to backApprox when increasing its size. (Note that interpolation is also used to find the best values
for pixels when an image is decreased in size.) The other types of interpolation supported by imresizeare "nearest neighbor" (the default), and "bicubic." For more information on interpolation and resizing
operations, see Interpolation and Image Resizing.
5. Subtract the Background Image from the Original Image
Now subtract the background image from the original image to create a more uniform background. First, change
the storage class of I to double, because subtraction can only be performed on double arrays.
I = im2double(I); % Convert I to storage class of double.
Now subtract backApprox256 from I and store it in a new array, I2.
I2 = I - backApprox256; % Subtract the background from I.
Subtracting backApprox256 from I may yield some out-of-range values in the image. To correct the dynamic
range of pixel values, use the max and min functions to clip pixel values outside the range [0,1].
I2 = max(min(I2,1),0); % Clip the pixel values to the valid range.
Now display the image with its more uniform background.
Step 6. You used the imadjust command to increase the contrast in the image. imadjust takes an
input image and can also take two vectors: [low high] and [bottom top]. The output image iscreated by mapping the value low in the input image to the value bottom in the output image, mapping
the value high in the input image to the value top in the output image, and linearly scaling the values in
between. See the reference pages for imadjust for more information.
The expression max(I2(:)) that you entered as the high value for the input image uses the MATLAB max
command to reshape I2 into a single column and return its maximum pixel value.
7. Apply Thresholding to the Image
Create a new binary thresholded image, bw, by comparing each pixel in I3 to a threshold value of 0.2.
Grand total is 327744 elements using 2621952 bytes
Here's What Just Happened
Step 7. You compared each pixel in I3 with a threshold value of 0.2. MATLAB interprets this
command as a logical comparison and therefore outputs values of 1 or 0, where 1 means "true" and 0means "false." The output value is 1 when the pixel in I3 is greater than 0.2, and 0 otherwise.
Notice that when you call the whos command, you see the expression logical listed after the class for
bw. This indicates the presence of a logical flag. The flag indicates that bw is a logical matrix, and the
Image Processing Toolbox treats logical matrices as binary images. Thresholding using MATLAB's
logical operators always results in a logical image. For more information about binary images and the
Thresholding is the process of calculating each output pixel value based on a comparison of the
corresponding input pixel with a threshold value. When used to separate objects from a background,
you provide a threshold value over which a pixel is considered part of an object, and under which a
pixel is considered part of the background. Due to the uniformity of the background in I3 and its high
contrast with the objects in it, a fairly wide range of threshold values can produce a good separation of
the objects from the background. Experiment with other threshold values. Note that if your goal were tocalculate the area of the image that is made up of the objects, you would need to choose a more precise
threshold value -- one that would not allow the background to encroach upon (or "erode") the objects.
Note that the Image Processing Toolbox also supplies the function im2bw, which converts an RGB,
indexed, or intensity image to a binary image based on the threshold value that you supply. You could
have used this function in place of the MATLAB command, bw = I3 > 0.2. For example,
bw = im2bw(I3, 0.2). See the reference page for im2bw for more information.
8. Use Connected Components Labeling to Determine the Number of Objects in theImage
Use the bwlabel function to label all of the connected components in the binary image bw.
In this case, some grains of rice are touching, so bwlabel treats them as one object.
To add some color to the figure, display labeled using a vibrant colormap created by the hot function.
map = hot(numObjects+1); % Create a colormap.imshow(labeled+1,map); % Offset indices to colormap by 1.
Here's What Just Happened
Step 8. You called bwlabel to search for connected components and label them with unique numbers.
bwlabel takes a binary input image and a value of 4 or 8 to specify the "connectivity" of objects. The
value 4, as used in this example, means that pixels that touch only at a corner are not considered to be
"connected." For more information about the connectivity of objects, see Connected-Components
Labeling.
A labeled image was returned in the form of an indexed image, where zeros represent the background,and the objects have pixel values other than zero (meaning that they are labeled). Each object is given a
unique number (you can see this when you go to the next step,9. Examine an Object). The pixel values
are indices into the colormap created by hot.
Your last call to imshow uses the syntax that is appropriate for indexed images, which is,
imshow(labeled+1, map);
Because labeled is an indexed image, and 0 is meaningless as an index into a colormap, a value of 1
was added to all pixels before display. The hot function creates a colormap of the size you specify. We
created a colormap with one more color than there are objects in the image because the first color is
used for the background. MATLAB has several colormap-creating functions, including gray, pink,
copper, and hsv. For information on these functions, see colormap in the MATLAB Function
You can also return the number of objects by asking for the maximum pixel value in the image. For
example,
max(labeled(:))ans =
80
9. Examine an Object
You may find it helpful to take a closer look at labeled to see what bwlabel has done to it. Use the imcropcommand to select and display pixels in a region of labeled that includes an object and some background.
To ensure that the output is displayed in the MATLAB window, do not end the line with a semicolon. In
addition, choose a small rectangle for this exercise, so that the displayed pixel values don't wrap in the MATLAB
command window.
The syntax shown below makes imcrop work interactively. Your mouse cursor becomes a cross-hair when
placed over the image. Click at a position in labeled where you would like to select the upper left corner of aregion. Drag the mouse to create the selection rectangle, and release the button when you are done.
grain=imcrop(labeled) % Crop a portion of labeled.
We chose the left edge of a grain and got the following results.
imcrop can also take a vector specifying the coordinates for the crop rectangle. In this case, it does notoperate interactively. For example, this call specifies a crop rectangle whose upper-left corner begins at
(15, 25) and has a height and width of 10.
rect = [15 25 10 10];roi = imcrop(labeled, rect)
You are not restricted to rectangular regions of interest. The toolbox also has a roipoly command thatenables you to select polygonal regions of interest. Many image processing operations can be
performed on regions of interest, including filtering and filling. See Chapter 10, Region-BasedProcessing for more information.
10. Compute Feature Measurements of Objects in the Image
The imfeature command computes feature measurements for objects in an image and returns them in astructure array. When applied to an image with labeled components, it creates one structure element for each
component. Use imfeature to create a structure array containing some basic types of feature information forlabeled.
grain=imfeature(labeled,'basic')
MATLAB responds with
grain =
80x1 struct array with fields: Area Centroid BoundingBox
Find the area of the grain labeled with 51's, or "grain 51." To do this, use dot notation to access the data in the
Area field. Note that structure field names are case sensitive, so you need to capitalize the name as shown.
Find the smallest possible bounding box and the centroid (center of mass) for grain 51.
grain(51).BoundingBox, grain(51).Centroid
returns
ans =
141.5000 89.5000 26.0000 27.0000ans =
155.3437 102.0898
Create a new vector, allgrains, which holds just the area measurement for each grain. Then call the whoscommand to see how allgrains is allocated in the MATLAB workspace.
allgrains=[grain.Area];whos allgrains
MATLAB responds with
Name Size Bytes Class
allgrains 1x80 640 double array
Grand total is 80 elements using 640 bytes
allgrains contains a one-row array of 80 elements, where each element contains the area measurement of agrain. Check the area of the 51st element of allgrains.
allgrains(51)
returns
ans =
323
which is the same result that you received when using dot notation to access the Area field of grains(51).
Here's What Just Happened
Step 10. You called imfeature to return a structure of basic feature measurements for each thresholded
grain of rice. imfeature supports many types of feature measurement, but setting the measurementsparameter to basic is a convenient way to return three of the most commonly used measurements: the area,
the centroid (or center of mass), and the bounding box. The bounding box represents the smallest rectanglethat can contain a region, or in this case, a grain. The four-element vector returned by the BoundingBox field,
[141.5000 89.5000 26.0000 27.0000]
shows that the upper left corner of the bounding box is positioned at [141.5 89.5], and the box has a width
of 26.0 and a height of 27.0. (The position is defined in spatial coordinates, hence the decimal values. For
more information on the spatial coordinate system, see Spatial Coordinates.) For more information about
working with MATLAB structure arrays, see Structures in the MATLAB graphics documentation.
You used dot notation to access the Area field of all of the elements of grain and stored this data to a newvector allgrains. This step simplifies analysis made on area measurements because you do not have to use
field names to access the area.
11. Compute Statistical Properties of Objects in the Image
Now use MATLAB functions to calculate some statistical properties of the thresholded objects. First use max to
find the size of the largest grain. (If you have followed all of the steps in this exercise, the "largest grain" is actuallytwo grains that are touching and have been labeled as one object).
max(allgrains)
returns
ans =
749
Use the find command to return the component label of this large-sized grain.
biggrain=find(allgrains==749)
returns
biggrain =
68
Find the mean grain size.
mean(allgrains)
returns
ans =
275.8250
Make a histogram containing 20 bins that show the distribution of rice grain sizes.
Step 11. You used some of MATLAB's statistical functions, max, mean, and hist to return thestatistical properties for the thresholded objects in rice.tif.
The Image Processing Toolbox also has some statistical functions, such as mean2 and std2, which are
well suited to image data because they return a single value for two-dimensional data. The functionsmean and std were suitable here because the data in allgrains was one dimensional.
The histogram shows that the most common sizes for rice grains in this image are in the range of 300 to400 pixels.
Exercise 1 -- Some Basic Topics Where to Go From Here