Image Segmentation Algorithms
Comparison and Analysis of the segmentation Algorithms
Divya Varghese
M.Tech, Computer Science and Engg
Nitte Meenakshi Institute of
Technology
Bangalore, India
divyavarghese.123@gmail.com
Dr.Jharna Majumdar
Dean R&D, H.O.D computer science and
Engg
Nitte Meenakshi Institute of Technology
Bangalore, India
Jharna.majumdar@gmail.com
Abstract— Image segmentation is grouping of similar
pixels based on properties like color, intensity, texture,
depth, motion. Image segmentation and its performance
evaluation are very difficult but important problems in
computer vision. Because image segmentation is used for
further processing like object recognition and data
compression. In this paper we compare three different
region-based segmentation algorithms and find the best
image segmentation algorithm for an image, based on the
performance metrics. The first algorithm is Mean-shift
algorithm. Second is Watershed algorithm. Third is Fast
Scanning algorithm.
Index Terms— Fast Scanning, Mean-shift, performance
metrics, Segmentation, Watershed.
I. INTRODUCTION
Image Segmentation [2][3] involves partitioning an image
into a set of homogeneous and meaningful regions so that
the pixels in each partitioned region possess an identical set
of properties or attributes. Image segmentation algorithms
are generally based on two basic properties of intensity
values of image pixels: discontinuity and similarity.
Segmentation algorithms are based on different parameters
of an image like gray-level, colour, texture, depth or motion.
The result of image segmentation is a set of segments that
collectively cover the entire image. All the pixels in same
region are similar with respect to some property such as
color, intensity or texture. Adjacent regions differ with
respect to same characteristics.
A large number of
segmentation techniques are available. But, there is no
general algorithm that can excellently perform the
segmentation task. Available segmentation techniques
include thresholding, region-based, clustering, classifier,
neural network-based, MRF model based approaches [1].
Image segmentation is very important in digital
image processing and computer vision applications. It is
considered as most critical components of an image analysis
and pattern recognition system. It is also considered as an
important operation for meaningful analysis and
Mrs.Bhuvaneshvari S Patil
Asst.Professor, Computer Science and
Engg
Nitte Meenakshi Institute of Technology
Bangalore, India
interpretation of the images acquired. The extraction of
objects from the background of a digital image has been a
challenging task in the field of digital image processing. With
the increasing demand for complex image analysis and
interpretation, the demand for accurate segmentation of
images has also grown stronger and as a result many image
segmentation methods and algorithms have been developed
over the past few decades.
The goal of this paper is to analyze the function performance
of region-based segmentation methods. The segmentation
method is used as initial analysis of the image which is used as
input for further computer vision tasks like Object detection,
recognition, shape analysis, tracking ,Use of Artificial
Intelligence and Machine Learning. Here, we consider the
three general-purpose methods, mean-shift, Watershed, and
fast scanning algorithms. The mean-shift method, initially
considers a bin value for a window, and repeats the shifting
procedure till convergence. The watershed method constructs
the dams across the regions. The fast scanning method groups
the pixels into an existing cluster based on some merging
criterion.
The applications of image segmentation includes several
domains such as Medical Science, Analysis of Remotely
sensed Image, Fingerprint Recognition, and Traffic System
Monitoring and so on.
The paper is organized as follows: Section 2 presents the
algorithms for region-based segmentation. Section 3
introduces briefly the performance measurement details.
Section 4 shows Experiment results and analysis charts. A
brief conclusion is given in Section 5.
II. ALGORITHMS FOR REGION-BASED SEGMENTATION
Region-based segmentation is a technique for determining
regions directly. The basic properties for region-based
segmentation are:
 Segmentation must be complete, that is every
pixel must be in a region.
 Points in a region must be connected in some
predefined sense.

The new mean is calculated as,
1(6) + 1(7) + 6(6) + 7(7)
The different regions must be disjoint.
A. Mean – Shift Segmentation (MS)
Mean-shift is used for clustering of the pixels in a specified
range. The idea of mean-shift [4] is very simple and based on a
histogram of the image. Let X={x1,x2,…….xN} be a set of N
image pixels assuming property vectors V(xi). Let H(x) be the
image histogram. H(x) is an array of bins H0, H1, H2…..HNG.
Here, 0 is minimum gray-tone value and NG is the maximum
gray-tone value. A window of the histogram of continuous bins
of some fixed length ws is created. In general ws will be an odd
number.
The idea of mean-shift is to start with windows centered on
a random bin and shift the center of the bin according to the
data within it converges at a mode of the histogram. This mode
defines a cluster, and the process is repeated until all bins are
associated with one of the computed modes. The shifting
procedure is given by the following steps:


Initialize with a random seed that selects a bin and
select the window W centered on that bin.
Calculate the center of gravity or weighted mean wm
of the histogram values in window W.
wm = ∑ bi f ( bi )
b €W
i


where f ( bi ) is the count in bin bi normalized by
dividing by the sum of counts in all the bins of
window W.
Translate the search window W to the weighted mean
wm.
Repeat previous two steps till convergence.
=6
15
The convergence point for each window W is stored, and all
the pixel values which satisfy the clustering criteria are
grouped together.
B. Watershed Method (WM)
In the past years the watershed transformation has proven
to be a very useful and powerful tool for morphological image
segmentation. Idea of the watershed construction is simple. A
grey-scale picture is considered as a topographic relief. Every
pixel of this digital image is assigned to the catchment basin of
a regional minimum.
Numerous techniques for computing the watershed method
are present. The first who proposed immersion based
watershed algorithm are Beucher and Lantuejoul [5]. Another
approach for catchment basin computing is described by Vince
and Soille [6]. The authors simulate the flooding procedure.
Here, the water is coming up out of the ground and flooding
the catchment basins. The dams are built across the regions so
that the water does not overflow to neighbouring regions.
Another approach for computing watershed transformation is
based on rainfalling simulation proposed by Alina N. Moga,
Bodgan Cramariuc and Moncef Gabbouj [7].
We use the flooding procedure [8] for the watershed
segmentation technique. In this method, regions correspond to
the catchment basins and contours are determined by the
watershed lines.
The shift procedure is illustrated in figure1. In the figure a
window of length 9 of the histogram is shown with bins 1
through 9, centered on bin 5. The first 5 bins have count 0,
while the other 4 have nonzero values totalling to 15.
Figure 2: Image shows the watershed regions
shift
1
2
3
4
5
6
7
8
Initial window centre
Figure1: Represents the 1D histogram
9
As shown in Fig 2, there are three regions in watershed
segmentation method:
 Grey level values which represent the surface.
 Catchment basin which represent the segmented
region.
 Watershed lines or dams which represent the
contours or boundaries of the region.
The procedure for flooding process is given as follows:







The pixels in an image are sorted in increasing order
of the gradient values.
Starting with the lowest gradient altitude, the water
gradually fills up the first catchment basin. Suppose
the flooding reaches a given level h.
Every catchment basin whose corresponding
minimum is smaller than or equal to h is assigned a
unique label.
The pixels with a gradient value h+1 are now
examined. If a pixel has a labeled pixel as its
neighbour, it is assigned the same label as its
neighbour.
If a pixel does not have a labeled pixel as its
neighbour, it corresponds to a local minimum at level
h+1.
This procedure is repeated until every pixel in the
image has been assigned a label.
The flooding procedure terminates when the level is
higher than the maximum gradient value and the
region boundaries are given by dams.
Figure 3: Steps in the flooding procedure.
The fig 3(a) shows the flooding procedure which takes place
from the minima and floods till it reaches a level h. fig 3(b)
shows when region is flooded the merging of regions takes
place and that is when a dam is constructed. Fig 3(c) and 3(d)
shows the construction of dams to prevent the merging of
regions.
C. Fast Scanning Method
The concept of fast scanning algorithm [9] is to scan from
the upper-left corner to lower-right corner of the whole image
and determine if we can merge the pixel into an existed
clustering. The merged criterion is based on our assigned
threshold. If the difference between the pixel value and the
average pixel value of the adjacent cluster is smaller than the
threshold, then this pixel can be merged into the cluster.
It is a simple concept and we list the steps of algorithm as
below. We use C[m, n] (m = 1, 2, …, M, n= 1, 2, …, N) to
denote the value of the pixel [m, n], we use R[m, n] to denote
the pixel [m, n] is classify into which region, use A(j) to
denote the mean of pixels in the jth region, and use B(j) to
denote the number of pixels in the jth region.
(Step 1): Classify the first pixel [1, 1] as Region 1, We set
R[1, 1] = 1, A(1) = C[1, 1], B(1) =1, m = 1, n = 1, and j = 1.
(Step 2): Then, set n = n+1 and scan the next pixel. If
R[1, n−1] = j and
 Case 1: if |C[m, n] − A(j)| ≤ threshold, (1) then set
R[m, n] = j and set
A(j) = {A(j) B(j) + C[m, n]}/( B(j) +1)
B(j) = B(j) +1. (2)
 Case 2: if |C[m, n] − A(j)| > threshold, (3) then set
R[m, n] = j+1, A(j+1) = C[m, n], B(j+1) = 1,
and j = j+1.
(Step 3): Repeat Step 2 until n = N.
(Step 4): Then, set m = m+1, n = 1, and scan the first pixel in
the next row.
(Step 6): Repeat Step 3 and Step 4 until all the pixels in the
image have been scanned.
(Step 7): If B(i) < Δ, we delete Region i and assign the pixels
in Region i to the adjacent regions. Sometimes, the isolated
dots (due to details or noise) of an image may cause oversegmentation. This step can avoid the problem.
(Step 8): Sort the regions according to B (i), i.e., the number
of pixels within them.
III. Performance Measure
Many segmentation methods have been developed,
but there is still no satisfactory performance measure, which
makes it hard to compare different segmentation methods. The
success or failure of computerized analysis procedures is
determined by the segmentation accuracy. In this section the
different performance metrics [10] which are used for finding
the accuracy of segmentation methods are discussed. The
probability function of the gray level image is estimated from
the percentages of the count at the specific level over the total
count across its histogram plot.
A. Gray Level Energy
The gray level energy indicates how the gray levels are
distributed. It is formulated as (1), where E(x) represents the
gray level energy with 256 bins and p(i) refers to the
probability distribution functions, which contains the
histogram counts. The energy reaches its maximum value of 1
when an image has a constant gray level.
k
E(x) =
∑ p (i) 2
(1)
i=1
The larger energy value corresponds to the lower number of
gray levels, which means simple. The smaller energy
corresponds to the higher number of gray levels, which means
complex.
B. Discrete Entropy
The discrete entropy is the measure of image information
content, which is interpreted as the average uncertainty of
information source. It is calculated as the summation of the
products of the probability of outcome multiplied by the log of
the inverse of the outcome probability, taking into
considerations of all possible outcomes {1, 2, …, n} in the
event {x1, x2, …, xn}, where n is the gray level; p(i) is the
probability distribution, considering all histogram counts. It is
formulated as (2).
k
H(x) =
∑ p (i) log 2 1
p(i)
(2)
R =
I(X ; Y)
H(x) + H(y)
(5)
IV. Experiment results and analysis charts
The tables below show the values obtained by the
segmentation methods for the various performance metrics.
i=1
For image processing, the discrete entropy is a measure of
how many bits are needed for coding the image data, which is
a statistical measure of randomness. The maximal entropy
occurs when all potential outcomes are equal. When the
outcome is certainty, the minimal entropy occurs which is
equal to zero. The discrete entropy represents average amount
of information conveyed from each individual image. It is the
fact that when the image pixels are distributed among more
gray levels, the entropy value will increase.
C. Mutual Information
The notion of the mutual information can be applied as
another objective metric. The mutual information acts as a
symmetric function, which is formulated in (3).
I(X;Y) = -∑ PX (x) log 2 PX (x) + ∑ PXY (x,y) log 2 PXY (x,y) (3)
x
x,y
PY (y)
= H(x) – H(x|y)
where I(X ; Y) represents the mutual information; H(X) and
H(X|Y) are entropy and conditional entropy values. It is
interpreted as the information that Y can tell about X is equal
to the reduction in uncertainty of X due to the existence of Y.
in image segmentation, the better match between the source
and processed images, the less value of the mutual
information.
D. Normalized Mutual Information
The normalized mutual information is a well defined measure
covering contents from both discrete entropies and mutual
information. It is formulated as (4).
I(X ; Y)
N_MI =
√ H(x) H(y)
(4)
where I(X, Y) is the mutual information; H(X) and H(Y) are
the discrete entropies. Once again, similar to that of the mutual
information, the better match between the source and
processed images, the smaller the normalized mutual
information.
TABLE 1: PERFORMANCE COMPARISON BASED ON TIME FOR EXECUTION
Images
Time taken for execution
Mean-shift
Watershed
Fast Scanning
1
0.078
0
1.469
2
0.078
0
1.469
3
0.078
0.016
1.391
4
0.078
0
2.235
5
0.079
0
1.703
From the Table 1, it is observed that in all the cases the fast
scanning algorithm takes more time for execution. The meanshift method takes almost the same time for all the images.
Watershed method is the fastest method which takes almost
negligible time for execution.
TABLE 2: PERFORMANCE COMPARISON BASED ON GREY LEVEL ENERGY
Images
Grey level energy values
Mean-shift
Watershed
Fast Scanning
1
0.059921
0.023758
0.05049
2
0.180893
0.371033
0.05049
3
0.131012
0.111008
0.26503
4
0.238693
0.183929
0.224838
5
0.301506
0.383707
0.306076
From the Table 2 it can be viewed that in most of the cases the
mean-shift method has higher grey level energy compared to
other methods. Higher grey-level energy refers to the lower
number of grey levels.
TABLE 3: PERFORMANCE COMPARISON BASED ON DISCRETE ENTROPY
E. Information Redundancy
Another symmetric information measure can be used to
indicate redundancy in image segmentation. It reaches the
minima of zero when all variables are independent. It is
formulated as (5). Bigger is the information redundancy,
greater the dependence between two images.
Images
Discrete entropy values
Mean-shift
Watershed
Fast Scanning
1
2.531187
1.770925
0.940478
2
4.770844
4.157135
0.940478
3
4.214459
2.657318
1.303519
Images
Discrete entropy values
Mean-shift
Watershed
Fast Scanning
TABLE 6: PERFORMANCE COMPARISON BASED ON INFORMATION
REDUNDANCY
Images
4
3.116527
3.005952
1.610933
5
4.517675
2.901501
1.886162
Information redundancy values
Mean-shift
From the table 3 values obtained above, it can be concluded
that mean-shift has greater discrete entropy value compared to
other two methods. Entropy indicates the degree of
randomness or uncertainty.
Watershed
Fast Scanning
1
0.287847
0.103024
0.635131
2
0.33998
0.075184
0.635131
3
0.354571
0.341729
1.336725
4
0.437975
0.066982
0.292611
5
0.558267
0.35884
1.121955
TABLE 4: PERFORMANCE COMPARISON BASED ON MUTUAL INFORMATION
Images
Mutual information values
Mean-shift
Watershed
Fast Scanning
1
2
0.637502
2
2
5.611849
1.19488
2
3
5.287725
5.564091
8.892962
4
5.180542
0.153376
1.745973
5
9.775905
5.703765
9.405248
From the table 4, it can be viewed that the mean-shift method
has greater mutual information compared to the other
methods. The greater match between the source and processed
images indicates a lesser value of mutual information. So from
the above table, watershed method shows greater match with
the source image.
TABLE 5: PERFORMANCE COMPARISON BASED ON NORMALIZED INFORMATION
Images
Normalized information values
Mean-shift
Watershed
Fast Scanning
1
0.598145
0.22794
1.387743
2
0.749991
0.171071
1.387743
3
0.787472
0.855992
3.367751
4
0.994224
0.22794
0.65911
5
1.275958
0.928939
2.686783
From the table 5, it can be viewed that fast scanning method
has greater normalized information compared to the other
methods. The greater match between the source and processed
images indicates a lesser value of normalized information. So
from the above table, watershed method shows greater match
with the source image.
From the table 6, it is observed that the watershed algorithm
has the lowest value for information redundancy and fast
scanning method has the highest value for information
redundancy. Greater the value of information redundancy,
greater is the dependence between the two images.
V. Conclusion
In this paper we have seen the different segmentation
techniques namely mean-shift, watershed, and fast scanning
methods. We used the different performance metrics to find
out the best segmentation algorithm. The tabular values for the
performance functions show that watershed segmentation is
the best of the three algorithms.
Watershed method is the best algorithm because it
takes very less time for the execution. It has very less value
for mutual information performance metrics which says it
matches with the source image. It has comparatively less value
for discrete entropy which means it has less uncertainty or
randomness. It also has a less value for normalized
information which is again a measure of matching with the
source image. Thus we conclude from the results of the above
performance metrics that the watershed based segmentation
algorithm is the best segmentation algorithm when compared
to mean-shift and fast scanning algorithm.
Figure 4: The segmentation results of mean-shift,
watershed,
and fast scanning algorithm
[5] S.Beucher and C.Lantuejoul, “Use of watersheds in contour
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