A novel embedded hybrid thinning algorithm for

InternationalJournal ofof Computer Science and Engineering
International Journal Computer Science and Engineering Research and Development (IJCSERD), ISSN
Research and Development (IJCSERD), ISSN 2248-9363 (Print), IJCSERD
2248-9363 (Print), ISSN 2248-9371 (Online) Volume 1, Number 2, May-October (2011)
ISSN 2248-9371 (Online), Volume 1,
Number 2, May-October (2011)
© PRJ PUBLICATION
pp. 01-07 © PRJ Publication
http://www.prjpublication.com/IJCSERD.asp

A NOVEL EMBEDDED HYBRID THINNING ALGORITHM FOR
IMAGE ENHANCEMENT AND ANALYSIS

Sri.K.Venkat Rao
Associate Professor, Dept. of CS&SE,AUCE,Visakhapatnam, India
Dr.I.Ramesh Babu
Professor and Head of the Dept., CSE, Nagarjuna University, Guntur, India

ABSTRACT

Image thinning is a morphological operation used in enhancing the image contrast.
Edges of multiple pixel width are thinned to single pixel width .Many algorithms are
proposed to thin images. Stentiford thinning algorithm and Zhan-Tsuen are two of them.
This paper proposes a hybrid algorithm combining the above two algorithms. This hybrid
approach is found to be more effective than the other two. Its efficiency is tested by
comparing the result with those of the other two algorithms.

Keywords: Stentiford Thinning,zhang-suen thinning,Hybrid Thinning

INTRODUCTION

Digital images have been widely used in a growing number of applications and the
effort to improve the images has been focused mostly to improve visual perception of
images that are unclear because of a variety of reasons. Image processing is any form of
signal processing for which the input is an image, such as photographs or frames of
video; the output of image processing can be either an image or a set of characteristics or
parameters related to the image[9]. Digital image processing is the use of
computer algorithms to perform image processing on digital images. As a subfield
of digital signal processing, digital image processing has many advantages over analog
image processing; it allows a much wider range of algorithms to be applied to the input
data, and can avoid problems such as the build-up of noise and signal distortion during
processing. Most image-processing techniques involve treating the image as a two-
dimensional signal and applying standard signal-processing techniques to it. Images can
be processed by optical, photographic, and electronic means, but image processing
using digital computers is the most common method because digital Methods are fast,
flexible, and precise. Image enhancement improves the quality (clarity) of images for
human viewing.

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International Journal of Computer Science and Engineering Research and Development (IJCSERD), ISSN

FUNDAMENTAL STEPS IN DIGITAL IMAGE PROCESSING

The fundamental steps in digital image processing involves many stages; image
acquisition, enhancement, restoration, color image processing, wavelets, compression,
morphological processing, segmentation, representa0tion and description as well as
recognition. Every process need not be applied to an image. The intention is to convey an
idea of all the methodologies that can be applied to images for different purposes and
possibly with different objectives.
The steps involved in Digital Image processing are pictographically depicted as
follows

Fig 1.1 Key stages in Digital Image Processing

1.1 Morphology
The word morphology commonly denotes a branch of biology that deals with the form
and structure of animals and plants. We use the same word here in the context of
mathematical morphology as a tool for extracting image components that are useful in the
representation and description of region shape, such as boundaries, skeletons, and the
convex hull. The language of mathematical morphology is set theory. As such,
morphology offers a unified and powerful approach to numerous image processing
problems. Sets in mathematical morphology represent objects in an image. For example,
the set of all black pixels in a binary image is a complete morphological description of
the image. In binary images, the sets in question are members of the 2-D integer space Z2,
where each element of a set is a tuple (2-D vector) whose coordinates are the (x,y)
coordinates of a black pixel in the image. Gray-scale digital images can be represented as
sets whose components are in Z3. In this case, two components of each element of the set
refer to the coordinates of a pixel, and the third corresponds to its discrete gray-level
value. Sets in higher dimensional spaces can contain other image attributes, such as color
and time varying components.

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Morphological Thinning
Thinning is a morphological operation that is used to remove selected foreground pixels
from binary images, somewhat like erosion or opening. It can be used for several
applications, but is particularly useful for skeletonization. In this mode it is commonly
used to tidy up the output of edge detectors by reducing all lines to single pixel thickness.
Thinning is normally only applied to binary images, and produces another binary image
as output. Like other morphological operators, the behavior of the thinning operation is
determined by a structuring element.
Thinning Methods
General
Thinning algorithms can be divided into two broad classes namely iterative and non-
iterative. Although non-iterative algorithms can be faster than iterative algorithms they do
not always produce accurate results.
Iterative Thinning
The Template-Based Mark-and-Delete Thinning Algorithms are very popular because of
their reliability and effectiveness. This type of thinning processes uses templates, where a
match of the template in the image, deletes the center pixel. They are iterative algorithms,
which erodes the outer layers of pixel until no more layers can be removed [3]. Almost all
iterative thinning algorithms use Mark-and-Delete templates including Stentiford
Thinning Method.
Both Stentiford and Zhang-Suen methods use Connectivity numbers to mark and delete
pixels.

Connectivity Number
The Connectivity number is a measure of how many objects are connected with a
particular pixel. The following is the equation to calculate Connectivity number.

Where: Nk is the colour of the eight neighbours of the pixel analyzed. N0 is the center
pixel. N1 is the colour value of the pixel to the right of the central pixel and the rest are
numbered in counter clockwise order around the center.
S = {1, 3, 5, 7}

Figure 2.3.1

The following are the connectivity numbers for Figure 2.3.1:
a) Connectivity number = 0.
b) Connectivity number = 1.
c) Connectivity number = 2.
d) Connectivity number = 3.
e) Connectivity number = 4.

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Stentiford Thinning Algorithm
It uses a set of four 3 x 3 templates to scan the image.
Figure 2.3.2 shows these four templates.

Figure 2.3.2

The Stentiford Algorithm can be stated as following:
1. Find a pixel location (i, j) where the pixels in the image match those in template T1.
With this template all pixels along the top of the image are removed moving from left to
right and from top to bottom.
2. If the central pixel is not an endpoint, and has connectivity number = 1, then mark this
pixel for deletion.
Endpoint pixel: A pixel is considered an endpoint if it is connected to just one other
pixel. That is, if a black pixel has only one black neighbour out of the eight possible
neighbours.
3. Repeat steps 1 and 2 for all pixel locations matching T1.
4. Repeat steps 1-3 for the rest of the templates: T2, T3, and T4.
T2 will match pixels on the left side of the object, moving from bottom to top and from
left to right. T3 will select pixels along the bottom of the image and move from right to
left and from bottom to top. T4 locates pixels on the right side of the object, moving from
top to bottom and right to left.
5. Set to white the pixels marked for deletion.

Zhang-Suen Thinning Algorithm
This skeletonization algorithm is a parallel method that means the new value obtained
only depend on the previous iteration value. It is fast and simple to be implemented. This
algorithm is made by two sub-iterations. In the fist one, a pixel I (i , j) is deleted if the
following conditions are satisfied [3]:
1. Its connectivity number is one.
2. It has at least two black neighbours and not more than six.
3. At least one of I(i,j+1), I(i-1,j), and I(i,j-1) are white.
4. At least one of I(i-1,j), I(i+1,j), and I(i,j-1) are white.

In the second sub-iteration the conditions in steps 3 and 4 change.

2. It has at least two black neighbours and not more than six.
3. At least one of I(i-1,j), I(i,j+1), and I(i+1,j) are white.
4. At least one of I(i,j+1), I(i+1,j), and I(i,j-1) are white.

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At the end, pixels satisfying these conditions will be deleted. If at the end of either sub-
iteration there are no pixels to be deleted, then the algorithm stops.[15]

Hybrid Thinning Algorithm

This algorithm is made by two sub iterations. In the first one, a pixel I(i,j) is deleted if the
following condition are satisfied.
2. It has at least two black neighbors and not more than six.
3. At least one of T4, T1, T2 matches.
4. At least one of T1, T3, T2 matches.

In the second iteration
2. It has at least two black neighbors and not more than six.
3. At least one of T1,T4,T3 matches.
4. At least one of T4,T3,T2 matches.

Analysis
An input image is thinned using stentiford thinning algorithm, Zhuan-Tsuen thinning
algorithm and then the proposed hybrid thinning algorithm.The results are shown below.

Fig:a Fig:b

Fig:c Fig:d
a:Input image
b:Output of Zhuan-Tsuen thinning algorithm
c:Output of Stentiford thinning algorithm
d:Output of Hybrid thinning algorithm

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Fig 3: Comparison of Time complexity of the three Algorithms

Hybrid approach is found to be having more time-complexity than the other two.But it is
more accurate. The results show that hybrid thinning algorithm is more effective in
increasing the contrast of the image.

CONCLUSION

This paper proposed a new hybrid morphological thinning algorithm.
Experimental results show that it is more accurate in thinning the image.However it does
not show any improvement regarding the time complexity.Future research can envisage
to improve time performance.

REFERENCES

[1] The Scientist and Engineer's Guide to Digital Signal Processing -By Steven
w.Smith,Ph.D
[2] Image processing:principles and applications
[3].Gonzalez and Woods (2001), Digital Image Processing, Vol. 2nd Edition, Prentice
Hall
[4] www.wikipedia.org
[5] Berthold Klaus Paul Horn. Robot Vision. MIT Press, 1986.
[6]http://homepages.inf.ed.ac.uk/rbf/HIPR2/open.htm
[7] Digital image processing - Bernd Jähne - Computers - 2005
[8] J.Serra. Image Analysis and Mathematical Morphology. Academic Press, 1982.
[9] An Introduction to Morphological Image Processing by Edward R. Dougherty
[10] Johnson, Philip, 1972. A History of Set Theory. Prindle, Weber & Schmidt

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[11] R. Haralick and L. Shapiro Computer and Robot Vision, Vol. 1, Addison-Wesley
Publishing Company, 1992, Chap. 5, pp 174 - 185.
[12] D. Vernon Machine Vision, Prentice-Hall, 1991, pp 78 - 79.
[13] Mathematical Morphology and its Applications to Image and Signal Processing,
Henk J.A.M. Heijmans and Jos B.T.M. Roerdink .
[14] R. Fisher, K Dawson-Howe, A. Fitzgibbon, C. Robertson, E. Trucco
(2005). Dictionary of Computer Vision and Image Processing. John Wiley.
[15] IMAGE PROCESSING TECHNIQUES FOR MACHINE VISION,Alberto Martin
and Sabri Tosunoglu

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