Introduction to Image Processing Steps of Image Processing Types of Image Processing Introduction to Image Segmentation Introduction to Medical Image Segmentation Application of Image Segmentation Example of Image Segmentation Need for Deformable Model What is Deformable Model?? Types of Deformable Model

1. PRESENTATION
ON
DEFORMABLE MODEL FOR
MEDICAL IMAGE
SEGMENTATION
Presented By
Subhash Basishtha
Assam University
Silchar

2. Presentation Outline
 Introduction to Image Processing
 Steps of Image Processing
 Types of Image Processing
 Introduction to Image Segmentation
 Introduction to Medical Image Segmentation
 Application of Image Segmentation
 Example of Image Segmentation
 Need for Deformable Model
 What is Deformable Model??
 Types of Deformable Model

3. Introduction to Image Processing
 Image processing is a method to convert an
image into digital form and perform some
operations on it, in order to get an enhanced
image or to extract some useful information
from it.
 It is a type of signal dispensation in which
input is image, like video frame or photograph
and output may be image or characteristics
associated with that image.

4. Steps of Image Processing
 Importing the image with optical scanner or by
digital photography.
 Analyzing and manipulating the image which
includes data compression and image
enhancement and spotting patterns that are
not to human eyes like tissue,satellite
photographs.
 Output is the last stage in which result can be
altered image or report that is based on image
analysis.

5. Phases of digital image
processing
[13]

6. Contd..
 Image processing Steps are:
1. Image Acquisition
2. Image Enhancement
3. Image Restoration
4. ColorImage Processing
5. Wavelets and Multiresolution Processing
6. Compression
7. Morphological Processing
8. Segmentation
9. Representation and Description
10. Object recognition
11. Knowledge Base

7. Purpose
The purpose of image processing is divided into 5
groups. They are:
 1. Visualization - Observe the objects that are not
visible.
 2. Image sharpening and restoration - To create a
better image.
 3. Image retrieval - Seek for the image of interest.
 4. Measurement of pattern – Measures various
objects in an image.
 5. Image Recognition – Distinguish the objects in
an image.

8. Types
The two types of methods used forImage
Processing are Analog and Digital Image
Processing.
 Analog or visual techniques of image
processing can be used for the hard copies
like printouts and photographs.
 Digital Processing techniques help in
manipulation of the digital images by using
computers.

9. Introduction to Image
Segmentation
 The purpose of image segmentation is to partition an image into
meaningful regions with respect to a particular application.
 The goal of segmentation is to simplify and/or change the
representation of an image into something that is more meaningful
and easier to analyze
 The segmentation is based on measurements taken from the image
and might be grey level, colour, texture, depth or motion.
 Usually image segmentation is an initial and vital step in a series of
processes aimed at overall image understanding.

10. Introduction to Medical Image
Segmentation
 An important goal of medical image processing is to
transform raw images into a numerically symbolic form for
better representation, evaluation, and /or content based
search and mining.
 With the advent of medical image modalities that provide
different measures of internal anatomical structure and
function, physicians are now able to perform typical clinical
tasks such as patient diagnosis and monitoring more safely
than before such imaging technologies existed.
 Computerized image segmentation has played an
increasingly important role in medical imaging.

11. Application of Image
Segmentation
 Segmented images are now used routinely in a
multitude of different applications, such as
- the quantification of tissue volumes
- diagnosis
- localization of pathology
- study of anatomical structure
- treatment planning and
- computer integrated surgery etc.

12. Application
[4]

13. Application
[5]

14. Example of Image Segmentation
 Example 1
 Segmentation based on greyscale
 Very simple ‘model’ of greyscale leads to inaccuracies
in object labelling

15. Example of Image Segmentation
 Example 2
 Segmentation based on texture
 Enables object surfaces with varying patterns of grey to be
segmented

16. Boundary Extraction example

17.  Dilation: Dilation is an operation that ‘grows’ or ‘thickens’ objects in
a binary image. Mathematically, dilation is defined in terms of set
operations. The dilation of A and B is defined as
 Erosion: Erosion is an operation that ‘Shrinks’ or ‘thins’ objects in a
binary image. The mathematical definition of erosion of A by B is as
Dilation and Erosion






≠∩=⊕ φABzBA z)(|
^
{ }φ≠∩=Θ C
z ABzBA )(|

18. Algorithm
 Add pixels to the boundaries of an image.
 Numbers of pixels added from the objetcs in
an image depends on the size of the
structuring Element.
 Function strel is used to generate the SE s.

19. Algorithm for Boundary Extraction
 Erosion Algorithm: The boundary of a set A, denoted
by (A)β , can be obtained by first eroding Aby B and
then performing the set differences between Aand its
erosion. That is,
(A)= A– (A B)β Θ
 Dilation Algorithm: The boundary of a set A, denoted
by (A)β , can be obtained by first dilating Aby Band
then performing the set differences between Aand its
dilation. That is,
(A)= (A B) – Aβ ⊕

20.  Boundary Extraction with the help of Dilation:
A=imread(‘a.jpg');
s=strel('disk',3);%Structuring element
F=imdilate(A,s); %Dialte the image by structuring element
figure,imshow(A);title('Original Image');
figure,imshow(F);title('Imdilate Image');
figure,imshow(F-A);title('Boundary extracted Image with using imdilate');
 Boundary Extraction with the help of Erosion:
A=imread('a.jpg');
s=strel('disk',3); %Structuring element
F=imerode(A,s); %Erode the image by structuring element
figure,imshow(A); title('Original Image');
figure,imshow(A-F); title('Boundary extracted Image with using imerode');
Matlab Practical

21. Need for Deformable Model
 Due to both the tremendous variability of object shapes and the
variation in image quality, Image Segmentation becomes a difficult
task.
 Problems do arise when medical images are corrupted with noise
and the structure itself is not clearly or completely visible in the
image.
 This may result in detecting erroneous object regions or
boundaries, or failing to detect true ones when applying classical
segmentation techniques such as edge detection and thresholding.
 To address these difficulties, deformable models have been
extensively studied and widely used in medical image
segmentation, with promising results.

22. What is Deformable Model??
 Deformable models analyze those noisy images and provide a
coherent representation for variable structure shapes.
 Deformable models are curves or surfaces defined within an image
domain that can move under the influence of internal forces and
external forces.
 Internal forces are defined within the curve or surface itself, and are
designed to keep the model smooth during deformation.
 External forces, are computed from the image data and are defined
to move the model toward an object boundary or other desired
features within an image.

23. What is Deformable Model??
 They are designed to be attracted to external image features
(such as edges) while maintaining internal shape constraints
(such as smoothness).
 By constraining extracted boundaries to be smooth and
incorporating other prior information about the object shape,
Deformable models offer:
- robustness to both image noise and boundary gaps.
 Deformable models allow integrating boundary elements into
a coherent and consistent mathematical description readily
available for subsequent applications.

24. How Become Popular
 The popularity of deformable model is largely due to the
seminar paper “Snakes:ActiveContours” by Kass,
Witkin, and Terzopoulos.
 Since its publication, deformable models have grown to
be one of the most active and successful research areas
in image segmentation.

25. Image Segmentation Using
Deformable Models
Fig: Variability of object shapes and imag equality. (a) A 2D MR image of the
heart
left ventricle and (b) a 3D MR image of the brain.

26. Image Segmentation Using
Deformable Models
Fig: Examples of using deformable models to extract object boundaries frommedical
images. (a) An example of using a deformable contourto extract the innerwall of the
ventricle of a human heart from a 2DMRimage. (b) (b)An example of using
a deformable surface to reconstruct the brain cortical surface froma 3DMRimage

27. Types of Deformable Model
 There are basically two types of deformable models
depending on how the model is defined in the shape
domain.:
a) parametric deformable models Or active contours,
b) geometric deformable models Or implicit models.

28. Parametric Deformable Models
 Parametric deformable models or active contours were
first introduced in 1988, by Kass et al., under the name
“snakes” .
 Snakes, use parametric curves to represent the model
shape.
 During their evolution, the deformations are determined
by geometry, kinematics, dynamics etc.

29. Parametric Deformable Models
 Parametric deformable models represent curves and surfaces
explicitly in their parametric forms during deformation.
 This representation allows direct interaction with the model and can
lead to a compact representation for fast real-time implementation.
 The main advantage of parametric models is that they are usually
very fast in their convergence, depending on the predetermined
number of control points.
 Problem:
Adaptation of the model topology, such as splitting or merging
parts during the deformation, can be difficult using parametric
models i.e it is topology dependent.

30. Parametric Deformable Models
 Two different types of formulations for parametric
deformable models:
- an energy minimizing formulation and
- a dynamic force formulation.
 The first formulation has the advantage that its solution
satisfies a minimum principle whereas the second
formulation has the flexibility of allowing the use of more
general types of external forces.

31. Internal Energy of Active
Contours:
 The internal energy of an active contour can
be translated as the summation of forces
applied along the curve to preserve its
smoothness.
 Mathematically
 Where the individual energies eint represent
the local state along the curve, and are
defined as,

32. External Energy of Active
Contours
 Common active contours use primarily edge
(image gradient) information to derive external
image forces that drive a shape based model.
External energy,
 where is the image Iafter smoothing
with a Gaussian kernel of standard deviation
, and is the image gradient along the curve
C.

33. Edge Based Active Contour
 Edge Detection is a well-developed field on its
own within image processing. Region
boundaries and edges are closely related,
since there is often a sharp adjustment in
intensity at the region boundaries.

34. Region Based Active Contour
 This method takes a set of seeds as input
along with the image. The seeds mark each of
the objects to be segmented.

35. Geometric Deformable Model
 Geometric deformable models, on the other hand, can
handle topological changes naturally.
 These model is based on the theory of curve evolution
and the level set method.
 These model represent curves and surfaces implicitly as
a level set of a higher-dimensional scalar function.
 Their parameterizations are computed only after
complete deformation, thereby allowing topological
adaptivity to be easily accommodated.

36. Geometric Deformable Model
 They use a distance transformation to define the shape
from the n-dimensional to an n +1 dimensional domain,
where n=1 for curves, n= 2 for surfaces on the image
plane,etc.

37. Region Based & Segment Based
 Region-based segmentation: Considers gray-
levels from neighboring pixels , either by
including similar neighboring pixels(region
growing), split-and-merge, or watershed
segmentation.
 • Edge-based segmentation: Detects edge
pixels and links them together to form
contours.

38. Region vs. edge-based
approaches
 Region based methods are robust because:
– Regions cover more pixels than edges and thus you have
more information available in order to characterize your
region
– When detecting a region you could for instance use texture
which is not easy when dealing with edges
– Region growing techniques are generally better in noisy
images where edges are difficult to detect
• The edge based method can be preferable because:
– Algorithms are usually less complex
– Edges are important features in a image to separate
regions. The edge of a region can often be hard to find
because of noise or occlusions

39. Conclusion
 In this paper, I have discussed medical image
segmentation methods, particularly
deformable model based methods, learning
based classification methods . Understanding
image content and extracting useful image
features are critical to medical image search
and mining.

40. Future Research
 We all are in midst of revolution ignited by fast
development in computer technology and
imaging. Against common belief, computers
are not able to match humans in calculation
related to image processing and analysis. But
with increasing sophistication and power of the
modern computing, computation will go
beyond conventional. Parallel and distributed
computing paradigms are anticipated to
improve responses for the image processing
results.

41. References
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MEDICAL IMAGES”,IEEE 2001.
 Xiaolei Huang “Medical Image Segmentation”, University of Miami
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http://www.uio.no/studier/emner/matnat/ifi/INF4300/h10/undervisningsmateriale/INF4300-2010-
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 Luis Ibfiez , Chafid Hamitouche , Christian Roux,” Structured Deformable Models Application of
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adaptive GMMRF model. European Conference on Computer Vision, 2004
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Deformable Model Framework for Homeomorphic 3D Medical Image Segmentation”
International Journal of Advanced Research in Computer Science and Software Engineering.

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43. THANK YOU