Medial axis transformation based skeletonzation of image patterns using image processing techniques

International Journal of Science and Research (IJSR), India Online ISSN: 2319‐7064
Volume 1 Issue 3, December 2012
www.ijsr.net
Medial Axis Transformation based Skeletonzation
of Image Patterns using Image Processing
Techniques
Navjot Mann1
, Parminder Singh2

1
Department of Computer Science & Engineering., SUS College of Engineering
Tangori, Mohali, Punjab INDIA
navjotmann.88@gmail.com
2
Department of Information Technology, Chandigarh Engineering College, Landran
Mohali, Punjab INDIA
singh.parminder06@gmail.com
Abstract: The medial axis of an image pattern is the loci of all inscribed disks that touch two or more boundary points without crossing
any of the boundaries. The medial axis transform (MAT) is a powerful representation for objects with inherent symmetry or near-
symmetry. The medial axis of 2-D image patterns provides a conceptual design base, with transition to a detailed design occurring when
the radius function is added to the medial axis or surface. To make such a design tool practicable, however, it is essential to be able to
convert from an MAT format to a boundary representation of an object. In the proposed work, the medial axis transform has been
extracted using the Euclidean distance transform based computation. The image pattern u prepared initially in binary form and then
distance of each non-zero pixel to its closest zeroed pixel is computed. This process continues till the entire image pattern is scanned to its
core.
Keywords: Medial Axis Transform, Euclidean Distance Transform, Charge Coupled Device
1. Introduction
Skeletonization is a process of extracting a skeleton from
an object in a digital image. A skeleton of an image can be
thought of as a one-pixel thick line through the middle of
an object which preserves the topology of that object. It is
a fundamental preprocessing step in many image
processing and pattern recognition algorithms.
Skeletonized images (skeletons) are easier to process and
they reduce processing time for the subsequent operations.
It is a morphological operation that is used to remove
selected foreground pixels from binary images. It is
somewhat like erosion or opening. It is particularly useful
for thinning and Medial Axis Transform. It is only applied
to binary images, and produces another binary image as
output. This operation makes use of a structuring element.
These elements are of the extended type meaning they can
contain both ones and zeros. The skeletonization operation
is related to the hit-and-miss transform and can be
expressed quite simply in terms of it. The skeletonization
of an image I by a structuring element J is given as:
= I – hit or miss (I, J)
2. Brief Literature Survey
Thinning algorithm uses flag map and bitmap
simultaneously to decide if a boundary pixel can be
deleted as well as incorporation of smoothing templates to
smooth the final skeleton. Various performance
measurements are proposed and their comparisons and
analysis are presented [5]. The images interested in a
scene can be characterized by structures composed of line
or curve or arc patterns for shape analysis. It is used to
compress the input data and expedite the extraction of
image features [3]. A good fingerprint thinning algorithm
can depress image noise and promote the robustness of the
minutiae extraction algorithm which helps improve the
overall performance of the system. Many thinning
algorithms have been devised and applied to a wide range
of applications including, Optical Character Recognition
(OCR), biological cell structures and fingerprint patterns
[10]. Service assets are proposed to be framed into a well-
established categorical structure based on pattern
recognition algorithm. This design aims to provide
systematic methodology and enablement architecture for
analyzing, clustering, and adapting heterogeneous services
for dynamic application integration [13]. Parallel thinning
algorithms which generate one-pixel-wide skeletons
generally have difficulty in preserving the connectivity of
an image or generate spurious branches. A thinning
algorithm for roman, devnagri and gurumukhi numerals
has been implemented and evaluated [8].
3. Image Preparation
Before extracting the medial axis transform of an image
pattern, the image is first converted to binary image with
white as background and black as object’s pixels. The
image can be binarized using the Otsu algorithm. In the
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presented work, the image is acquired using CCD camera
in jpeg format. The jpeg format is converted to gray scale
image using rgb2gray command in matlab. Otsu algorithm
is then applied over the gray scale image in order to get
the binary image. The binary image is now ready to face
the MAT algorithm in order to extract the skeleton of the
image pattern under test.
3. Euclidean Distance Transform
The distance transform provides a metric or measure of
the separation of points in the image. The Euclidean
distance is the straight-line distance between two pixels.
0 0 0
0 1 0
0 0 0
The bwdist function in matlab computes the Euclidean
distance between each pixel that is set to off (0) and the
nearest nonzero pixel for bi nary images. The Euclidean
distance between points p and q is the length of the line
segment connecting them ( ). In Cartesian coordinates,
if P = (p1, p2,..., pn) and Q = (q1, q2,..., qn) are two points
in Euclidean n-space, then the distance from p to q, or
from q to p is given by:
D(P,Q) = √ (P1 - Q1)2 + (P1 - Q1)2 + . . . (Pi - Qi)2 + . . .
(Pn - Qn)2
Below images show the Euclidean distance (ED)
transform as computed in matlab:
Figure 1: Input Image
Figure 2: ED Transform
4. Extraction of MAT
The medial axis transform (MAT) of an image is
computed by calculating the Euclidean distance transform
of the given input image pattern. The MAT is described as
being the locus of the local maxima on the distance
transform.
After the computation of Euclidean distance transform
(EDT) of the input image, the EDT is represented in
image representing the Euclidean distances as gray levels.
The same is shown in above images.
The maximum Euclidean distance is represented as
maximum gray level intensity in the EDT image. The
pixel coordinates of the maximum gray level intensity are
extracted from the EDT image by converting the EDT
image into row x column matrix. The row and column of
the matrix gives the coordinates of the MAT line of the
image pattern.
5. Performance Evaluation Parameters
Connectivity number (CN):
It is a measure of how many objects are connected with a
particular pixel [3]. It is measured by number of color
changes (black to white or white to black) in
neighbourhood of pixel P[i][j]
Cn= Nk – (Nk+1 . Nk+2)
where Nk = color of eight neighbours of pixel
analyzed
N0 = central pixel
N1 = Pixel to the right of the central pixel and
so on
Thinness Measurement (TM):
It measures the degree to which an object in an image is
thinned [6].
TM = 1 – (TM1 / TM2)
TM1 = Triangle Count (P[i][j])
TM2 = 4 × [max(m,n) – 1]2
Where Triangle Count (P[i][j]) = P[i][j] × P[i][j-1] ×
P[i-1][j-1] +
P[i][j] × P[i-1][j-1] × P[i- 1][j]
+
P[i][j] × p[i-1][j] × P[i-1][j+1]
+
P[i][j] × P[i-1][j+1] × P[i][j+1]
And TM1 = Total number of black triangles in an
image.
TM2= Maximum number of black triangles an
image could have
Triangle Count = 2
Connectivity Measurement (CM):
It is used to measure the connectivity of output skeleton.
An object in an image is said to be disconnected if it has
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broken pieces. [6]
CM = S(P[i][j])
Where S(P[i][j]) =
Sensitivity Measurement (SM):
It is used to measure the noise in an image. This noise is
mainly the perturbations in an outline of an object, not
unwanted extra parts in an image. [6]
SM =
Where S(P[i][j]) =
6. Results
Following images show the input image pattern and the
corresponding MAT.
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Input Images – Fig. 3, 5 and 7
MAT Images – Fig. 4, 6 and 8
Table 1: Performance measure of the skeletonized image
Image No. CN TM CM SM
Fig. 3 0.0114 0.880 0.00013 0.005
Fig. 5 0.0209 0.984 0.00015 0.103
Fig. 7 0.0300 0.896 0.00040 0.014
7. Conclusion
The result table shows the results after implementing the
discussed medial axis transform based skeletonzation of
image patterns. The Skeletonized images are shown in fig.
4, 6 and 8. Higher the thinness factor better is the
skeletonzation or thinning. The discussed algorithm has
been implemented on matlab version 7.5 and a text file is
generated after every skeletonization.The performance
parameters are normalized with respect to size of the
image. This removes the ambiguity of the image if
zoomed or compressed. Euclidean transform based medial
axis transformation extraction of image patterns give a
good speedy skeleton of image patterns. This serves good
purpose in terms of computational speed.
References
[1] Mark Vincze, Bence kovari: “Comparitive survey of
thinning algorithms”, Budapest University of
Technology and economics.
[2] Khalid Sayeed, Marek Tabe˛Dzki , Mariusz Rynnik
Marcin Adamski :“K3M: A Universal Algorithm For
Image Skeletonization and a Review Of Thinning
Techniques”, Int. J. Appl. Math. Computer Sci., 2010,
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Vol. 20, No. 2, 317–335 DOI: 10.2478/v10006-010-
0024-4
[3] Dr. P.Subashini, S.Jansi: “Optimal thinning algorithm
for detection of FCD in MRI images”, International
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[4] Louisa Lam, Seong-Whan lee, Member, IEEE, and
Ching Y.Suen fellow, IEEE:”Thinning methodologies
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pattern analysis and machine intelligence, Vol 14,
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[12]Jacob Graves, Roger Mailler:”Quatifying the
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Author Profile
Navjot Kaur received the degree in Computer Science
and Engineering from Yadawindra College of Engineering
in 2010. She is pursuing Masters Degree in Information
Technology from Chandigarh Engineering College,
Landran, Mohali from 2010 that is under thesis. She is
working in SUS college of Engineering, Tanori, Mohali in
Computer Science Department as Assistant Professor.
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Medial axis transformation based skeletonzation of image patterns using image processing techniques

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