As technology grows day by day computer vision becomes a vital field of understanding the behavior of an image. Image segmentation is a sub field of computer vision that deals with the partition of objects into number of segments. Image segmentation found a huge application in pattern reorganization, texture analysis as well as in medial image processing. This paper focus on distinct sort of image segmentation techniques that are utilized in computer vision. Thus a survey has been created for various image segmentation techniques that describe the importance of the same. Comparison and conclusion has been created within the finish of this paper.

1. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
83 NITTTR, Chandigarh EDIT-2015
A Survey on Image Segmentation and its
Applications in Image Processing
Kamaljeet Kainth1, Amritpal Singh2
, Priya Chitkara3
1,2,3
Dept. of ECE, Guru Nanak Dev University Regional Campus, Jalandhar, India
1
kainth108@gmail.com, 2
asdhunna91@gmail.com, 3
chitkara23@gmail.com
Abstract: As technology grows day by day computer vision
becomes a vital field of understanding the behavior of an
image. Image segmentation is a sub field of computer vision
that deals with the partition of objects into number of
segments. Image segmentation found a huge application in
pattern reorganization, texture analysis as well as in medial
image processing. This paper focus on distinct sort of image
segmentation techniques that are utilized in computer vision.
Thus a survey has been created for various image
segmentation techniques that describe the importance of the
same. Comparison and conclusion has been created within
the finish of this paper.
Keywords: Image segmentation, Image processing,Fuzzy-C
Means (FCM) ,Edge based segmentation.
I. INTRODUCTION
Image segmentation plays an important role in computer
vision. Pattern recognition, texture analysis, medical image
processing, facial detection and many security system
deployed segmentation technique. These segmentation
techniques are broadly classified into following categories,
Edge based segmentation, Fuzzy based detection, PDE
based segmentation, Region based segmentationThreshold
based segmentation .Edge detection is one of the major
tool for image segmentation. Edge detection is the
approach for detecting significant discontinuities in
intensity values. In gray level main discontinuities are
point, lines and edges. In order to find the discontinuities
these techniques utilize are Robert edge detection, Log
Gabor edge detection [1], Sobel edge detection, Prewitt
edge detection and canny edge detection. An image is
segmented on the basis of gray level discontinuities which
are present on the boundary the image [2].Fuzzy based
detection applies the principle of clustering. Clustering is
the process in which similar objects are grouped into one
cluster. Fuzzy clustering is one step ahead to hard (crisp)
clustering and mainly employed for acquiring fuzzy
patterns. Fuzzy clustering is further categorized into fuzzy-
c means (FCM) and fuzzy kernel c-means algorithms
(KFCM) [3]. In FCM distance between data points are
considered to form a cluster and on the basis of this cluster
centers are created [4]. However KFCM overcomes the
disadvantage of FCM and kernel information is added to
FCM, to cover small differences between clusters. Partial
differential equation (PDE) based segmentation is an
efficient technique for image segmentation. PDE based
model is a geometrical active counter model which utilize
fuzzy classification and can handle variation in topology of
shape [5]. This model overcomes the disadvantages of
fuzzy based detection hence a better option for image
segmentation. Region based segmentation employ the
approach of dividing the image into small regions. These
regions are created on the basis of color, texture, intensity
value. The segmented regions are assigned with a label.
Region based segmentation uses region transformation or
histogram method. This approach provides advantage of
continuous segmentation maps of closely related objects
[6]. Threshold values for detecting corresponding area are
enforced for efficient segmentation.Threshold based
segmentation method is the simplest method for
segmentation. In this method threshold values are obtained
by histogram of the edges of the image. Global
thresholding, variable thresholding and multiple
thresholding technique are employed to select the threshold
value. A threshold selection component is defined to select
the threshold value which is automatically updated
according to the contrast of the edges detected. But this
technique has a disadvantage that this cannot be applied to
complex images as partitioning of pixels is difficult
[7].This paper will survey all the techniques employed for
segmentation purposes.
II. LITERATURE SURVEY
Shinn-Ying Ho and Kual-Zheng Lee[8]. , proposed a
novel approach which satisfies the basic objectives of
image segmentation i.e. an algorithm must have an
efficient segmented contour , computation time must be
very small without setting any threshold and segmented
regions should be segmented robustly. This efficient
technique is termed as evolutionary image segmentation
algorithm. This technique employ K-means algorithm
which is utilized to split an image into several
homogeneous regions. This particular algorithm also
overcomes the issues which are faced by traditional image
segmentation techniques like, over segmentation,
continuous contour. The overall algorithm is split into two
major categories named as split procedure and merge
procedure respectively. First one groups the pixels of small
region into the larger adjacent region. Second one utilized
binary chromo sense channel encoding and further dived
into seven steps to achieve the above mention objectives.
The whole scenario reduces the computation time,
improves the quality of splinted image and hence results
into efficient robust segmentation techniques in
comparison to traditional segmented technique.Various
threshloding techniques have been developed for image
segmentation in [9]. H.D. Cheng et.al in [10], presented an
image segmentation technique which is based on
homogram thresholding in conjunction with region
merging technique. First one consider local and global
information whereas later one finds peaks of homogram
.Region merging gives free space and spatial relation
between pixels. Further this paper compare histogram
thresholding approach their approach which proves that
afterward one estimate more information about gray level

2. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 84
of an image and also provides spatial information. On the
other hand histogram thresholding analyze only spatial
dependencies amongst pixels when entropy is calculated
.Hence present approach is much effective and provides
better segmentation results.
Mahmoud R. Rezaee et.alin [11], suggested an invincible
segmentation technique which employ pyramid image
segmentation followed by fuzzy c-means (FCM) clustering
algorithm. First of all the image is segmented into distinct
regions by pyramid segmentation algorithm [8], which
obtain global view of an image representation and also
acquires location of all the objects in the image. Afterward
fuzzy c- means clustering algorithm [13]performs feature
analysis, shape analysis and clustering. Authors claims that
this combined algorithm have several advantages over
traditional image segmentation techniques which are listed
as: a) cut downs computational overhead, b) can be
enforced to N-dimension images, c) protect information; d)
segmented image is employed to fuzzy system.Shijuan He,
et.al in [14] introduces a MRI image segmentation
algorithm established on histogram and fuzzy c-means
techniques. This algorithm is dived into sub steps i.e. a)
pre-processing has been done to remove extracranial tissue
to develop brain image, b) initial centroids, c) connectivity
based segmentation for leveling of homogenous regions of
an image. This integrated algorithm overcomes the issue
like overlapping of intensity distribution, partial volume
effect. Apart of great advantages the overall algorithm
enhances the complexity of the overall algorithm.Gour C.
Karmakar and Laurence S. Dooley in [15] introduced a
novel generic fuzzy rule based image segmentation
(GFRIS) which overcomes the issue of previous
segmentation technique defined in [6] FCM is sensitive to
parameter value selection. This algorithm is overcomes
this issue and manipulate its advantages to determine inter
pixel spatial relation. This algorithm divided into major six
steps elaborated as i) classification of pixels ii) deduce the
threshold and key weight iii) arrange center of regions, iv)
calculation of membership function , v) employ fuzzy role
to compute pixel into region ,vi) classify the pixel and
return to step (iv) if it fails to do so . Further comparison
has been made among FCM, possibilistic c-means and
GFRIS. Hence it controls the maximum permitted pixel
intensity variation and enhances the inherent concurrency.
Ying-Tung Hsiao et.al in [16] proposed a novel approach
for segmentation of graph based images. They utilized
morphological edge detection technique (MED) with
another technique named as region growing or integrated
version of both. Erosion and dilution morphological
operators an applied also known as shirking and expansion
operators respectively. Later region growing technique
[17] is employed. After applying mathematical closing
region merging technique is applied. The whole scenario
decreases the noise and as a result it enhances the edge
features. Also it reduces the redundant regions which do
not have adequate edge points between them to cut down
the overall cost.Anastasia Sofou et.al in [18], presented an
image segmentation technique which is based on
morphological Partial Differential Equation (PDE) along
with watershed transom segmentation [15] to analyze
intensity contrast and region size criteria of an image.
Further this algorithm analyzes the modulation texture
features with multiple cues. Since our main goal is to
examine segmentation techniques used in segmentation we
did not further analyze the paper as it is more concerned
about the feature analysis. However, authors claimed that
to have better segmentation result by employing multiple
cues rather than utilizing single cue. Anil K. Jain and
Michael E. Farmer in [20], provides an alternative
segmentation method top overcome the issues faced by
conventional segmentation algorithms. Conventional
algorithms employs segmentation accompanied by
classification hence confronts some issues defined as: i)
there is no prior knowledge about the object which is to be
extracted; ii) there are no proper metrics which are
previously defined for comparison of image segmentation
algos ad iii) all the techniques are failed to adapt real world
challenges. Hence they proposed a new method which
combines segmentation and classification in one step hence
named as wrapper method [21] in conjunction with filter
method. This method performs image segmentation,
feature extraction and classification in one go and hence
turn s out to be an algorithm which is much effective and
resolves all the above issues.During survey we found an
exciting paper which actually demonstrating the edge
based segmentation technique on a hardware setup.
Krzysztof Strzecha et.al in [21] presents a computerized
system for the measurement of temperature (high) of
superficial properties. Before this most of the methods
which measures surface properties need operators to take
observation and make a record of it. But this technology
automates the process for the measurement of various
surface quantities values. Jinsheng Xiao et.al in [22],
introduces a nonlinear PDE for the processing of gray scale
images. Conventional morphological images detect edges
for the segmentation but lags due to sensitive gradient
operators. Hence before segmentation, smoothening of an
image is required to overcome this problem. But also liner
something of an image results into blurred edge of the
segmented image. So the proposed nonlinear discontinue
PDE method can solve this issue hence provide better
results for segmentation of morphological gray scale
images. Hui Zhang et.al in [23] , presents survey on the
unsupervised methods of image segmentation. As in [18] it
is stated that supervised methods needs an observer to take
care of all records and data which is not an efficient
method. Hence there is need of unsupervised methods
which automate this sector. Hence this paper elaborate
about the various supervised method, system level method
[24], analytical method [25] and unsupervised methods.
Hence they conclude that proposed method is much
effective than the supervised method and also overcome its
issues which are mentioned above.Wen-Xiong Kang, et.al
in [25], provides a base for the thorough study of image
segmentation and its techniques along with the benefits
and imperfections. They focused on segmentation as well
as evaluation techniques for the same .They concern about
conventional segmentation algorithms that are edge based,
region based and special theory based. Furthermore, they
covered evaluation techniques which are analytical [26]
and experiment techniques [27] respectively. Analytical
techniques measure the segmentation algorithm by
examining the principle of algorithm, whereas later one is
employed to comparison of results which are obtained by
various experiments on the image. Hence this paper
delivers a comparison between all segmentation techniques

3. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
85 NITTTR, Chandigarh EDIT-2015
and their future use. Siti N. Sulaiman et.al in [28] proposed
a novel algorithm which is not restricted only up to
specific images rather one can do real world image
segmentation, i.e. segmentation of images which are
captured by any CCTV camera, digital cameras etc. can be
possible. Han Lili et.al in [29] proposed an image
segmentation algorithm for medical images. They utilized
edge segmentation with prewitt operator and Hough
transform. They extract contour of objects from medical
images. Hence this is an efficient way to segment medical
images and extract objects. For example, it can be tissues
of heart. Image restoration is also a major application of
the image processing. M. Erdt et.al in [30] presents a dual
technique of medical image segmentation and
restoration.S. Sridevi and Dr. M. Sundaresan, in [31]
explored a new area of image segmentation i.e.
segmentation of Ultrasound images. Ultrasound imaging is
also termed as sonography and used to test tissues of a
human body.
TABLE I: Comparison of various segmentation techniques
S.No. Segmentation
technique
Advantages Disadvantages
1.
Edge based[29] Easy to detect
edges and their
orientation
Sensitive to noise
and inaccurate
2.
Region
based[30]
Easy to detect
small regions
Some tolerance is
required.
3.
Threshold
based[28]
Technique
work well even
in presence of
noise.
Incorrect pixel
added to region
can’t differentiate
properly
4. Fuzzy based[31]
Able to detect
small variations
in intensity
Not able to
differentiate
noise
5. ANN based Does not
require prior
information of
image to
segment it.
Changes in
location of
centroid provide
different results.
6.
Sobel, prewitt
edge
detection[32]
Detection of
edges and their
orientation is
simple
Sensitive to noise
7.
Canny edge
detection
Improve S/ N
ratio ,
Complex
computations and
time consuming
III. CONCLUSION
A survey has been compiled on distinct segmentation
approaches. On the basis of survey various advantages and
disadvantages has been made in TABLE I. Indeed this
field of image processing has vast number of application in
computer vision, associated fields and in medical image
processing.
REFERENCES
[1]. Siti N. Sulaiman and Nor A. M. Isa , “Adaptive Fuzzy-K-means
Clustering Algorithm for Image Segmentation”,pp.2661-2668, 2010.
[2]. Wen-Xiong Kang, Qing-Qiang , Run-Peng Liang, “The comparative
research on image segmentation algorithms,” International Workshop
on Education Technology and Computer Science, pp. 703-708,2009.
[3]. Wen-XiongKang,Qing-Qiang , Run-Peng Liang, “The comparative
research on image segmentation algorithms,” International Workshop
on Education Technology and Computer Science, pp. 703-708,2009.
[4]. A.Sofou and P.Maragos, “PDE-based modeling of image
Segmentation using volume flooding”, IEEE ICIP, 2003.
[5]. Kaganami, H.G., Zoubeiji, “Region-based segmentation versus Edge
Segmentation”, Intelligent Information Hiding and Multimedia Signal
Processing, pp 1217-1221, 2009.
[6]. Shiping Zhu, Xi Xia &et al, “An Image Segmentation Algorithm in
Image Processing Based on Threshold segmentation”, IEEE (SITIS),
pp. 673-678, 2007.
[7]. Claudio Rosito Jung, “Multiscale image segmentation using wavelets
and watersheds,” IEEE Symp.2003, Computer Graphics and Image
Processing, SIBGRAPI XVI Brazilian Symposium, pp.278–284,
2003.
[8]. Shinn-Ying Ho and Kual-Zheng Lee, “An Efficient Evolutionary
Image Segmentation Algorithm”, IEEE Conf., pp.1327-1334,2001.
[9]. J. E Canny, “A computational approach to edge detection,” IEEE
Trans. on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679-
698, 1986.
[10]. H.D. Cheng , X.H. Jiang and Jingli Wang, “Color image
segmentation based on homogramthresholding and region merging” ,
Pattern Recognition 35, pp. 373-393, (2002) .
[11]. Mahmoud R. Rezaee et.al , “A Multiresolution Image Segmentation
Technique Based on Pyramidal Segmentation and Fuzzy Clustering” ,
IEEE Transactions On Image Processing, VOL. 9, NO. 7, pp.1238-
1248, 2000.
[12]. R. Pal and S. K. Pal, “A review in image segmentation techniques”,
Pattern Recognition, vol. 26, pp. 1277-1294, 1993.
[13]. A. Boudraa, “Automated detection of the left ventricular region in
magnetic resonance images by Fuzzy C-means model,” International
journal. Cardiac Image. VOL. 13, pp. 347–355, 1997.
[14]. Shijuan He , Xia Wang and Yamei Yang , “MRI Brain Images
Segmentation” , IEEE Conf. , pp. 113-116, 2000
[15]. P. J. Burt, “The pyramid as a structure for efficient computation,” in
Multiresolution Image Processing and Analysis, A. Rosenfeld, Ed,
Berlin, Germany: Springer-Verslag, pp. 6–35 ,1984 .
[16]. Gour C. Karmakar and Laurence S. Dooley , “A generic fuzzy rule
based image segmentation algorithm”, Pattern Recognition Letters 23
,pp. 1215–1227,2003.
[17]. Wen-Bing Tao, Jin-Wen Tian, Jian Liu, “Image segmentation by
three-level thresholding based on maximum fuzzy entropy and
genetic algorithm” , Pattern Recognition Letters , pp.3069–3078,
2003 .
[18]. Y.L. Chang and X. Li, “Adaptive image region-growing,” IEEE
Trans. Image Processing, vol. 3, pp. 868–872, 1994.
[19]. Anastasia Sofou, GeorgiosEvangelopoulos, and Petros Maragos,
“Coupled Geometric And Texture PDE-Based Segmentation”,2005.
[20]. L. Vincent and P. Soille, “Watershed in digital spaces: An efficient
algorithm based on immersion simulations,” IEEE Transaction,
PAMI, pp. 583-598, 1991.
[21]. EtyNavon, Ofer Miller and Amir Averbuch, “Color image
segmentation based on adaptive local thresholds” , Image and Vision
Computing 23 ,pp. 69–85 , 2005 .
[22]. Krzysztof Strzecha, Anna Fabijan'ska, DominikSankowski ,
“Application Of The Edge-Based Image Segmentation”,
MEMSTECH'2006, pp.28-31, 2006.
[23]. Jinsheng Xiao et.al, “An image segmentation algorithm based on
level set using discontinue PDE”, First International Conference on
Intelligent Networks and Intelligent Systems, pp. 503-506, 2008.
[24]. HuiZhang, Jason E. Fritts and Sally A. Goldman, “Image
segmentation evaluation: A survey of unsupervised methods” ,
Computer Vision and Image Understanding 110 ,pp. 260–280 , 2008 .
[25]. Y. Zhang, A survey on evaluation methods for image segmentation,
Pattern Recognition 29 (8) ,pp.1335–1346, 1996.
[26]. ShitalRaut et.al, “Image Segmentation – A State-Of-Art Survey for
Prediction” , International Conference on Advanced Computer
Control , pp.420-424,2008
[27]. Brink A. D, “Thresholding of digital images using of two
dimensional entropies” ,Pattern Recognition, vol.25 (8), pp.803–808,
1992.
[28]. Siti N. Sulaiman and Nor A. M. Isa , “Adaptive Fuzzy-K-means
Clustering Algorithm for Image Segmentation”, pp.2661-2668, 2010.
[29]. Han Lili, Ma Wanjun and Ning Yi, “The Study of Image Edge
Segmentation based on Hough Transform”, 2011.
[30]. M. Erdt, S. Steger and G. Sakas, “Regmentation: A New View of
Image Segmentation and Registration” , Journal of Radiation
Oncology Informatics, pp. 1-23 , 2012. .
[31]. S. Sridevi and Dr. M. Sundaresan “Survey Of Image Segmentation
Algorithms On Ultrasound Medical Images” ,pp.215-220,2013.