The document discusses implementing the watershed algorithm for image segmentation using MATLAB. It begins with an overview of the watershed algorithm which uses region growing to segment images based on gradients. It then discusses image segmentation goals and different categories of segmentation algorithms including discontinuity, similarity, thresholding and region-based approaches. The document outlines steps to be taken which include preprocessing images, implementing the watershed algorithm, and analyzing results to see the difference in segmented images. It expects watershed processing to produce segmented regions from a test image.

1. Study and
Implementation of
Watershed Algorithm
using MATLAB
Supervisor– Prof. Sanjeev Kumar By– Mukul Jindal
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2. Watershed Algorithm
The watershed transformation is a technique for
segmenting digital images that uses a type of
region growing method based on an image gradient. It
thus effectively combines elements from
both the discontinuity and similarity methods described
below.
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3. What is Image Segmentation
The goal of image segmentation is to reduce the number of colours in the input
reference image and then group neighbouring pixels of similar colour together to
form bounded segments
Segmentation subdivides an image into its constituent regions or groups.
The level to which the subdivision is carried depends on the problem being
solved.
That is, segmentation should stop when the objects of interest in an application
have been isolated.
e.g. automated inspection of electronic assemblies; specific anomalies; missing
components or broken connection paths.
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4. Image Segmentation
algorithm
It is based on two basic properties of intensity values :
discontinuity and similarity
First Category : Abrupt changes in intensity.
Second Category : Partitioning of regions which are
similar according to a set of predefined criteria. e.g.
thresholding, region growing, region splitting and merging.
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5. First Category is further subdivided
into-
•Points
•Lines
•Edges
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6. Detection of discontinuities
Points, lines, edges
The most common way
R = w1*z1 + w2*z2 + ……+ w9*z9
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7. Point detection
R  T
T = Threshold
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8. Point detection
(b) X-ray image (c) Result of (d) Result of point
of a turbine blade point detection detection mask
with porosity mask with threshold
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9. Line detection
– A Suitable Mask in desired direction
– Thresholding
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10. Line detection
• Example:
-45º Mask Thresholding
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11. Edge Detection
– Two Mathematical model
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12. Edge Detection
Gray level
profile
First
derivative
Second derivative
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13. Gradient Operators
Y-direction X-direction
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14. Diagonal Edge
45-Direction
-45-Direction
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15. Diagonal edge detection
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16. Things done so far
• Read about different Image Segmentation processes.
• Working my way towards implementing Watershed
algorithm using MATLAB.
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17. Things to be done
• Use preprocessing method to be implemented on
images.
• Implement Watershed Algorithm
• Analyse and record the difference after processing.
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18. Test Result Expected from Watershed
Algorithm
Test image After Watershed Algorithm
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19. References -
• Paul R. Hill. Wavelet Based Texture Analysis and
Segmentation for Image Retrieval and Fusion. PhD thesis,
University of Bristol, March 2002.
• Richard E. Woods and R.C. Gonzalez. Digital Image
Processing. Pearson Education, 2005.
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