imsegkmeans3
K-means clustering based volume segmentation
Description
Examples
Segment Volume Using k-Means Clustering
Load a 3-D grayscale MRI volume and display it using volshow
.
load mristack
volshow(mristack);
Segment the volume into three clusters.
L = imsegkmeans3(mristack,3);
Display the segmented volume using volshow
. To explore slices of the segmented volume, use the Volume Viewer app.
figure volshow(L);
Input Arguments
V
— Volume to segment
3-D grayscale volume | 3-D multispectral volume
Volume to segment, specified as a 3-D grayscale volume of size m-by-n-by-p or a 3-D multispectral volume of size m-by-n-by-p-by-c, where p is the number of planes and c is number of channels.
Note
imsegkmeans3
treats 2-D color images like 3-D volumes of size
m-by-n-by-3. If you want 2-D behavior, then
use the imsegkmeans
function.
Data Types: single
| int8
| int16
| uint8
| uint16
k
— Number of clusters
positive integer
Number of clusters to create, specified as a positive integer.
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: 'NumAttempts',5
NormalizeInput
— Normalize input data
true
or 1
(default) | false
or 0
Normalize input data to zero mean and unit variance, specified as the
comma-separated pair consisting of 'NormalizeInput'
and a numeric or
logical 1
(true
) or 0
(false
). If you specify true
, then
imsegkmeans3
normalizes each channel of the input
individually.
NumAttempts
— Number of times to repeat the clustering process
3
(default) | positive integer
Number of times to repeat the clustering process using new initial cluster
centroid positions, specified as the comma-separated pair consisting of
'NumAttempts'
and a positive integer.
MaxIterations
— Maximum number of iterations
100
(default) | positive integer
Maximum number of iterations, specified as the comma-separated pair consisting of
'MaxIterations'
and a positive integer.
Threshold
— Accuracy threshold
1e-4
(default) | positive number
Accuracy threshold, specified as the comma-separated pair consisting of
'Threshold'
and a positive number. The algorithm stops when each
of the cluster centers move less than the threshold value in consecutive
iterations.
Output Arguments
L
— Label matrix
matrix of positive integers
Label matrix, specified as a matrix of positive integers. Pixels with label 1 belong
to the first cluster, label 2 belong to the second cluster, and so on for each of the
k
clusters. L
has the same first three
dimensions as volume V
. The class of L
depends
on number of clusters.
Class of L | Number of Clusters |
---|---|
'uint8' | k <= 255 |
'uint16' | 256 <= k <= 65535 |
'uint32' | 65536 <= k <= 2^32-1 |
'double' | 2^32 <= k |
centers
— Cluster centroid locations
numeric matrix
Cluster centroid locations, returned as a numeric matrix of size
k-by-c, where k is the number
of clusters and c is the number of channels.
centers
is the same class as the image
I
.
Tips
The function yields reproducible results. The output will not vary in multiple runs given the same input arguments.
References
[1] Arthur, David, and Sergei Vassilvitskii. “K-Means++: The Advantages of Careful Seeding.” In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–35. SODA ’07. USA: Society for Industrial and Applied Mathematics, 2007.
Version History
Introduced in R2018b
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