dice
Sørensen-Dice similarity coefficient for image segmentation
Description
computes the Sørensen-Dice similarity coefficient between binary images
similarity
= dice(BW1
,BW2
)BW1
and BW2
.
computes the Dice index for each label in label images similarity
= dice(L1
,L2
)L1
and
L2
.
computes the Dice index for each category in categorical images
similarity
= dice(C1
,C2
)C1
and C2
.
Examples
Compute Dice Similarity Coefficient for Binary Segmentation
Read an image with an object to segment. Convert the image to grayscale, and display the result.
A = imread('hands1.jpg'); I = im2gray(A); figure imshow(I) title('Original Image')
Use active contours (snakes) to segment the hand.
mask = false(size(I)); mask(25:end-25,25:end-25) = true; BW = activecontour(I, mask, 300);
Read in the ground truth segmentation.
BW_groundTruth = imread('hands1-mask.png');
Compute the Dice index of the active contours segmentation against the ground truth.
similarity = dice(BW, BW_groundTruth);
Display the masks on top of each other. Colors indicate differences in the masks.
figure
imshowpair(BW, BW_groundTruth)
title(['Dice Index = ' num2str(similarity)])
Compute Dice Similarity Coefficient for Multi-Region Segmentation
This example shows how to segment an image into multiple regions. The example then computes the Dice similarity coefficient for each region.
Read an image with several regions to segment.
RGB = imread('yellowlily.jpg');
Create scribbles for three regions that distinguish their typical color characteristics. The first region classifies the yellow flower. The second region classifies the green stem and leaves. The last region classifies the brown dirt in two separate patches of the image. Regions are specified by a 4-element vector, whose elements indicate the x- and y-coordinate of the upper left corner of the ROI, the width of the ROI, and the height of the ROI.
region1 = [350 700 425 120]; % [x y w h] format
BW1 = false(size(RGB,1),size(RGB,2));
BW1(region1(2):region1(2)+region1(4),region1(1):region1(1)+region1(3)) = true;
region2 = [800 1124 120 230];
BW2 = false(size(RGB,1),size(RGB,2));
BW2(region2(2):region2(2)+region2(4),region2(1):region2(1)+region2(3)) = true;
region3 = [20 1320 480 200; 1010 290 180 240];
BW3 = false(size(RGB,1),size(RGB,2));
BW3(region3(1,2):region3(1,2)+region3(1,4),region3(1,1):region3(1,1)+region3(1,3)) = true;
BW3(region3(2,2):region3(2,2)+region3(2,4),region3(2,1):region3(2,1)+region3(2,3)) = true;
Display the seed regions on top of the image.
imshow(RGB) hold on visboundaries(BW1,'Color','r'); visboundaries(BW2,'Color','g'); visboundaries(BW3,'Color','b'); title('Seed Regions')
Segment the image into three regions using geodesic distance-based color segmentation.
L = imseggeodesic(RGB,BW1,BW2,BW3,'AdaptiveChannelWeighting',true);
Load a ground truth segmentation of the image.
L_groundTruth = double(imread('yellowlily-segmented.png'));
Visually compare the segmentation results with the ground truth.
figure
montage({label2rgb(L),label2rgb(L_groundTruth)})
title('Comparison of Segmentation Results (Left) and Ground Truth (Right)')
Compute the Dice similarity index for each segmented region. The Dice similarity index is noticeably smaller for the second region. This result is consistent with the visual comparison of the segmentation results, which erroneously classifies the dirt in the lower right corner of the image as leaves.
similarity = dice(L, L_groundTruth)
similarity = 3×1
0.9396
0.7247
0.9139
Input Arguments
BW1
— First binary image
logical array
First binary image, specified as a logical array of any dimension.
Data Types: logical
BW2
— Second binary image
logical array
Second binary image, specified as a logical array of the same size as
BW1
.
Data Types: logical
L1
— First label image
array of nonnegative integers
First label image, specified as an array of nonnegative integers, of any dimension.
Data Types: double
L2
— Second label image
array of nonnegative integers
Second label image, specified as an array of nonnegative integers, of the
same size as L1
.
Data Types: double
C1
— First categorical image
categorical
array
First categorical image, specified as a categorical
array of any
dimension.
Data Types: category
C2
— Second categorical image
categorical
array
Second categorical image, specified as a categorical
array of the
same size as C1
.
Data Types: category
Output Arguments
similarity
— Dice similarity coefficient
numeric scalar | numeric vector
Dice similarity coefficient, returned as a numeric scalar or numeric
vector with values in the range [0, 1]. A similarity
of
1 means that the segmentations in the two images are a perfect match. If the
input arrays are:
binary images,
similarity
is a scalar.label images,
similarity
is a vector, where the first coefficient is the Dice index for label 1, the second coefficient is the Dice index for label 2, and so on.categorical images,
similarity
is a vector, where the first coefficient is the Dice index for the first category, the second coefficient is the Dice index for the second category, and so on.
Data Types: double
More About
Dice Similarity Coefficient
The Dice similarity coefficient of two sets A and B is expressed as:
dice
(A,B) =
2 * |
intersection
(A,B)
| / ( | A | + | B | )
where |A| represents the cardinal of set A. The Dice index can also be expressed in terms of true positives (TP), false positives (FP) and false negatives (FN) as:
dice
(A,B) =
2 * TP / ( 2 * TP +
FP + FN)
The Dice index is related to the Jaccard index according to:
dice
(A,B) =
2 * jaccard
(A,B) /
(1 + jaccard
(A,B)
)
Version History
Introduced in R2017b
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