Marker controlled watershed segmentation ,Watershed one of the most important method in image segmentation has interesting properties that make it useful for much different image segmentation application

1. Marker controlled
watershed
segmentation
November 2018
Eng.Ansam Hadi Rashed
Al- Mustansiriya university
college of engineering
Computer Department

2. Watershed one of the most important method in
image segmentation has interesting properties
that make it useful for much different image
segmentation application. Watershed is a powerful
technique for rapid detection of both edges and
regions. The watershed transformation is a powerful
tool for image segmentation based on mathematical
morphology.
What is watershed?

3. Marker controlled watershed
• Image segmentation is significance problem in different fields of
computer vision and image processing. Image segmentation is the
process of partitioning a digital image into multiple segments
knows as set of pixels. The goal of segmentation is to simplify
change the representation of an image into something that is
more meaningful and easier to analyze.
• Segmentation by watershed transform is a fast, robust and widely
used in image processing and analysis, but it suffers from over-
segmentation, Over segmentation means a large number of
segmented regions.

4. Marker controlled watershed
• An approach used to control over segmentation is based on the
concept of markers. A marker is a connected component
belonging to an image.
• Markers are of two types internal and external, internal for
object and external for boundary. The marker-controlled
watershed segmentation has been shown to be a robust and
flexible method for segmentation of objects with closed contours.
• the boundaries of the watershed regions are arranged on the desired
ridges, thus separating each object from its neighbors.

5. Marker controlled watershed
• The basic idea of marker-controlled
watershed transform is that flooding
the topographic surface from a previously
defined set of markers.The gradient image
is viewed as topographic surface, thus,
pixels with low gradient values will be
flooded with high priority.
• results obtained show the good
performance of this approach specially in
medical branch.

6. Marker controlled watershed in simple example
1- consider this simple image I. It contains two primary regions, the
blocks of pixels containing the values 13 and 17. The background is
primarily all set to 10, with some pixels set to 11.

7. Marker controlled watershed in simple example
2- create a marker image Mr is to subtract a constant h from the mask
image I. the constant h is very important, it depends on the processed
image, and we must choose the right value of this constant. In this
example we choose h=2 then Mr = I – 2

8. Marker controlled watershed in simple example
3-In the output image Ire, note how all the intensity fluctuations except
the intensity peak have been removed .In the output image, all insignifi
cant local maxima will be deleted.

9. Marker controlled watershed in simple example
morphological reconstruction, consider this simple image. It contains t
wo primary regions, the blocks of pixels containing the values 14 and 1
8. The background is primarily all set to 10, with some pixels set to 11.

10. Marker controlled watershed in simple example
• Create a marker image

11. Marker controlled watershed in simple example
Call the imreconstruct function to morphologically reconstruct the imag
e. In the output image, note how all the intensity fluctuations except the
intensity peak have been removed.
recon = imreconstruct(marker, mask)
Recon=

12. Summarization of watershed steps
1. Compute a segmentation function. This is an image whose dark
regions are the objects you are trying to segment.
2. Compute foreground markers. These are connected blobs of pixels
within each of the objects.
3. Compute background markers. These are pixels that are not part of
any object.
4. Modify the segmentation function so that it only has minima at the
foreground and background marker locations.
5. Compute the watershed transform of the modified segmentation
function

13. Marker controlled watershed in simple exampleB-watershed
D-watershedwithmarker

14. Watershed VS Marker controlled watershed
• The result obtained by watershed without markers gives no
information on the regions of the original image. Against the
result obtained by watershed with markers shows the speed of
segmentation.
• Marker controlled method detects all important objects of the origin
al image , and the number of regions obtained was decreased.
• The problem of local minima is eliminated then the problem of over
-segmentation is solved.
• the markers are used to control the watershed to obtain good
results.

15. Watershed transformation process
1-Read Color Image and Convert it to Gray scale
rgb = imread('pears.png');
I = rgb2gray(rgb);
imshow(I)
text(732,501,'Image courtesy of Corel(R)',...
'FontSize',7,'HorizontalAlignment','right')

16. Watershed transformation process
2-Use the Gradient Magnitude as the Segmentation Function
The gradient is high at the borders of the objects and low (mostly) inside the objects.
hy = fspecial('sobel');
hx = hy';
Iy = imfilter(double(I), hy, 'replicate');
Ix = imfilter(double(I), hx, 'replicate');
gradmag = sqrt(Ix.^2 + Iy.^2);
figure
imshow(gradmag,[]), title('Gradient magnitude (gradmag)')

17. Watershed transformation process
3-Mark the Foreground Objects
se = strel('disk',20);
Io = imopen(I,se);
imshow(Io) title('Opening')
compute the opening-by-reconstruction
using imerode and imreconstruct
Ie = imerode(I,se);Iobr = imreconstruct(Ie,I)
;imshow(Iobr)title('Opening-by-Reconstruction')

18. Watershed transformation process
Following the opening with a closing can remove the dark spots and stem marks.
Ioc = imclose(Io, se);
figure
imshow(Ioc), title('Opening-closing (Ioc)')
Notice we must complement the image inputs
and output of imreconstruct.
Iobrd = imdilate(Iobr,se);
Iobrcbr = imreconstruct(imcomplement(Iobrd)
,imcomplement(Iobr));
Iobrcbr = imcomplement(Iobrcbr);
imshow(Iobrcbr)title('Opening-Closing by Reconstruction')

19. Watershed transformation process
Calculate the regional maxima of Iobrcbr to obtain good foreground markers.
fgm = imregionalmax(Iobrcbr);
imshow(fgm)
title('Regional Maxima of Opening-Closing by Reconstruction')
superimpose the foreground marker image
on the original image.
I2 = I;
I2(fgm) = 255;
figure
imshow(I2),
title('Regional maxima superimposed on original image (I2)')

20. Watershed transformation process
cleaning the edges of the marker blobs and then shrinking them a bit
se2 = strel(ones(5,5));
fgm2 = imclose(fgm, se2);
fgm3 = imerode(fgm2, se2);
fgm4 = bwareaopen(fgm3, 20);
I3 = I;
I3(fgm4) = 255;
figure
imshow(I3)
title('Modified regional maxima superimposed
on original image (fgm4)')
• Compute Background Markers
Compute Background Markers,
Starting with thresholding operation
bw = imbinarize(Iobrcbr);
figure
imshow(bw),
title('Thresholded opening-closing by reconstruction (bw)')

21. Watershed transformation process
The background pixels are in black, but ideally we don't want the background markers to be too close to the edges
of the objects we are trying to segment.
We'll "thin" the background by computing
the "skeleton by influence zones", or SKIZ, of the
foreground of bw. This can be done
by computing the watershed transform of the
distance transform of bw, and then looking for the
watershed ridge lines (DL == 0)
D = bwdist(bw);
DL = watershed(D);
bgm = DL == 0;
figure
imshow(bgm), title('Watershed ridge lines (bgm)')
Compute the Watershed Transform of the Segmentation Function.
gradmag2 = imimposemin(gradmag, bgm | fgm4);
Finally we are ready to compute the watershed-based segmentation.
L = watershed(gradmag2);

22. Watershed transformation process
6-Visualize the Result
one of the techniques is to superimpose the foreground markers, background markers, and segmented object
boundaries.
I4 = I;
I4(imdilate(L == 0, ones(3, 3)) | bgm | fgm4) = 255;
figure
imshow(I4)
title('Markers and object boundaries superimposed
on original image (I4)')
Another useful visualization technique is to
display the label matrix as a color image
Lrgb = label2rgb(L, 'jet', 'w', 'shuffle');
figure
imshow(Lrgb)
title('Colored watershed label matrix (Lrgb)')

23. Watershed transformation process
We can use transparency to superimpose this pseudo-color label matrix on top of the original intensity image.
figure
imshow(I)
hold on
himage = imshow(Lrgb);
himage.AlphaData = 0.3;
title('Lrgb superimposed transparently on original image')

24. color image segmentation with marker controlled with watershed

25. conclusion
• The application of image processing has widely applied
in our life, Image segmentation is a key step for transition
to the image analysis as low-level processing in digital
Image processing .for this reasons we must find the optimal
method for image segmentation different segmentation
techniques are reviewed and found that marker based is best
in most of cases because it marks the regions then segment
them.
• It is a general method which can be applied in many situa
tions

26. Thank you
November 2018