IMAGE SEGMENTATION USING K-MEANS ALGORITHM
Submitted
By-
Puja Gupta
Registration no-161541810016
Roll no-15499016014
MASTER DEGREE THESIS
A thesis submitted in partial fulfilment of the requirements for
the degree of MSC
IN
Computer Science
Under Supervision
Subhajit Adhikari
Dinabandhu Andrews Institute of Technology and
Management
Maulana Abul Kalam Azad University of Technolodgy
11th MAY,2018
To whom it may concern
This is certified that the work entitled as ‘Image segmentation by K-
MEANS Algorithm’ has been satisfactory complete by Puja Gupta
(Registration no-161541810016 Roll no-15499016014).It is a bona-
fide work carried out under my supervision at DINABANDHU
ANDREWS INSTITUTE OF TECHNOLOGY AND
MANAGEMENT Kolkata for partial fulfilment of M.sc in computer
science during the academic year 2016-2018.
Project Guide
SubhajitAdhikari
Assistant professor
DINABANDHU ANDREWS INSTITUTE OF TECHNOLOGY
AND MANAGEMENT
Kolkata
Forward by
Paramita Ray
HOD of Computer science Dept
DINABANDHU ANDREWS INSTITUTE OF TECHNOLOGY
AND MANAGEMENT
Kolkata
CERTIFICATE AND APPROVAL
This is certified that the work entitled as ‘IMAGE SEGMENTATION
USING K-MEANS ALGORITHM’ has been satisfactory complete by
Puja Gupta (Registration no-161541810016 Roll no-
15499016014).It is a bona fide work carried out under my supervision
at DINABANDHU ANDREWS INSTITUTE OF TECHNOLOGY
AND MANAGEMENT Kolkata for partial fulfilment of M.sc in
computer science during the academic year 2016-2018.It is
understood that by this approval the undersigned do not necessarily
endure or approve any statement made, opinion expressed or
conclusion drawn there in but approve for which it has been
submitted.
Examiners
Signature of the examiner
Date:
DECLARATION OF ORIGINALITY AND
COMPLIANCE OF ACADEMIC ETHICS
I hereby declare that this thesis contents original research work done
by me, as part of master of computer science studies. All information
in this document has been obtained and presented in accordance with
the academic rules and ethical conduct.
I also declare that, as required by these rules and conduct I have fully
cited and referenced all the materials.
Name-Puja Gupta
Registration no-161541810016
Roll no-15499016014
Title- IMAGE SEGMENTATION USING K-MEANS
ALGORITHM
Signature:
Date:
ACKNOWLEDGEMENT
I would like to express my sincere, heartfelt gratitude to my respected
guide Assistant Prof. SUBHAJIT ADHIKARI department in
computer science in DINABANDHU ANDREWS INSTITUTE OF
TECHNOLOGY AND MANAGEMENT under MAKAUT, for his
unfailing guidance, prolific encouragement, constructive suggestions
and continuous involvement during each and every phase of this
work.
I would also thanks principle madam Prof. Dr. SANJUKTA NANDY,
and Assistant Prof. PARAMITA RAY’, HOD of the computer science
department, all faculty members and staff for providing me all the
facilities and for their support to all activities.
I would like to express my gratitude to my parents ‘LATE BIJAY
GUPTA and RADHA GUPTA’ for their unbreakable believe, support
and guidance.
Last but not the least I would like to thanks all my classmates of M.sc
Computer science batch 2016-2018for their co-operation and support.
Date:
Name-Puja Gupta
Registration no-161541810016
Roll no-15499016014
ABSTRACT
Image segmentation is the classification of an image into different
groups or regions. In this project, we want to do some prediction
about the features of image regions that will help us to find some
abnormality that can be present in an image for forensic study. So
segmentation is taken to partition the image into different clusters or
regions. K-means segmentation algorithm is used to find clusters from
an colour image. First, colour the image is taken and resized, then it is
converted it into grey scale image. Then k-means algorithm is used to
find different clusters and a .csv file is written.
INTRODUCTION
IMAGE[1] is a virtual representation, it is an extact replica of a
storage device.An image is defined as a two dimentional function,f(x,
y), where x and y are the coordinates of the function f and the
amplitudes of f at the coordinates (x, y) is called the gray level of the
image.
IMAGE PROCESSING-
When the co-ordinates (x, y) and the intensity values of the function f
are all finite ,then call the image as a digital image. In the field of
image processing [2] refers to processing digital images by means of a
digital computer. A digital image is composed of a finite number of
elements , it has a particular location and a value. These elements of
digital image are called picture elements ,image elements, pixel(used
to denote the elements of a digital image).
SEGMENTATION
Image segmentation [3] is the method of partitioning a digital image
into several segments. The goal of segmentation is to simplify and
change the representation of an image into something that is more
meaningful and easier to analyze.
In a region, each pixels are similar with some properties such as
colour, intensity or texture. The main goal of segmentation is to
divide an image into parts having strong correlation with areas of
interest in the image. Image segmentation can be done , based on two
properties of an image:
A. Discontinuities based method are used to partition an image
based on abrupt changes in intensity, this includes image
segmentation algorithm like edge detection.
B. Method based on similarity measure is used to divide an image
into constituent parts according to predefined criteria, this
includes image segmentation algorithms like thresholding,
region growing. Image segmentation is the process where a
digital image partition into multiple image.
APPLICATION OF IMAGE SEGMENTATION
Medical Image Segmentation
Medical image segmentation [3] is used in various applications. As
for example, in medical image analysis, segmentation is used to locate
tumours, analyze anatomical structure etc. It provides comparable
resolution and better contrast resolution.
K-Means Clustering Algorithm
Clustering is a method to divide a set of data into a specific number of
groups. It’s one of the popular method is k-means clustering. In k-
means clustering[4], it partitions a collection of data into a k number
group of data11, 12. It classifies a given set of data into k number of
disjoint cluster. Kmeans algorithm consists of two separate phases. In
the first phase it calculates the k centroid and in the second phase it
takes each point to the cluster which has nearest centroid from the
respective data point.
The DBSCAN Algorithm The density-based spatial clustering of applications with noise
algorithm, usually abbreviated as DBSCAN[5], is a recently
developed alternative method for clustering data sets. Unlike other
clustering algorithms that require many parameters, such as before the
computation the number of clusters should be defined, the DBSCAN
algorithm has only two input parameters: the minimum size of a
cluster and the maximum distance between points in a cluster. The
algorithm operates by cycling through all points in the data set and
calculating the number of neighbours each point has, which is defined
as the number of other points that are within the minimum distance of
the original point. Any data point that has fewer neighbours than the
minimum cluster size parameter is declared to be a noise point that is
not associated with any cluster.
COMPARASION BETWEEN K MEANS AND DBSCAN
ALGORITHM
K-means DBScan
Supervised Unsupervised
Number of clusters are required Number of clusters are not
required
Fast convergence Slow convergence
LITERATURE SURVEY
In this paper, they have discussed about block-based image segmentation. Here,
Image processing is following 3 stages-Reconstruction, Transformation and
Classification. [6]
In this paper, they have discussed about defect detection by K-means clustering.
Here, they have worked using automated segmentation, it is the most difficult
task in image analysis. [7]
In this paper, they have discussed about clustering,clustering algorithms
areclassified - Exclusive Clustering, Overlapping Clustering Hierarchical
Clustering, Probabilistic Clustering. Here, they have worked using K-means
segmentation. [8]
In this paper, they have discussed about the filtering algorithm, Data sensitive
analysis, Emperical analysis. [9]
In this paper, they have discussed about watershed segmentation,k-means
clustering algorithm, improved watershed segmentation algorithm, [10]
ALGORITHM
STEP 1.Read the image
STEP 2.Convert into grayscale
STEP 3.Resize the image
STEP 4.Perform K-means f(x,y) where x=data, y=no. of clusters
STEP 5.Write the clusters into a .csv file
1.Read the image
2.Convert into greyscale
3.Resize the image
Start
Stop
4. Perform K-means clustering:
BLOCK DIAGRAM
Read Image
Convert into
grayscale
Resize the image
Perform k-means
Function(x,y)
CONCLUSION & FUTURE SCOPE
I have segmented an image by using k-MEANS clustering algorithm.
First, I read the image and convert it into grey scale very carefully.
After resizing the image, I have implemented k-means clustering
algorithm. Many clusters are found. In future, i want to analyse the
different properties (e.g. shape) of each clusters to predict some
abnormality is present or not in the image for forensic study. .
REFERENCES
1.Bilen, Hakan. "image Processing." (2017).
2. Rafael C.Gonzalez and Richard E.Woods, “Digital Image Processing
(book)”.
3.Chandni Panchasara and Amol Joglekar , ”Application of Image
Segmentation Techniques on Medical Reports”, Chandni Panchasara et al, /
(IJCSIT) International Journal of Computer Science and Information
Technologies.
4.Dhanachandra, Nameirakpam, Khumanthem Manglem, and Yambem Jina
Chanu. "Image segmentation using K-means clustering algorithm and
subtractive clustering algorithm." Procedia Computer Science 54.2015 (2015):
764-771.
5.Dudik, Joshua M., et al. "A comparative analysis of DBSCAN, K-means, and
quadratic variation algorithms for automatic identification of swallows from
swallowing accelerometry signals." Computers in biology and medicine 59
(2015): 10-18
6.Zaitoun, Nida M., and Musbah J. Aqel. "Survey on image segmentation
techniques." Procedia Computer Science 65 (2015): 797-806.
7. Pham, Van Huy, and Byung Ryong Lee. "An image segmentation approach
for fruit defect detection using k-means clustering and graph-based
algorithm." Vietnam Journal of Computer Science 2.1 (2015): 25-33.
8. Patel, Piyush M., Brijesh N. Shah, and Vandana Shah. "Image segmentation
using K-mean clustering for finding tumor in medical
application." International Journal of Computer Trends and Technology
(IJCTT) 4.5 (2013): 1239-1242.
9. Kanungo, Tapas, et al. "An efficient k-means clustering algorithm: Analysis
and implementation." IEEE transactions on pattern analysis and machine
intelligence 24.7 (2002): 881-892.
10. Ng, H. P., et al. "Medical image segmentation using k-means clustering and
improved watershed algorithm." Image Analysis and Interpretation, 2006 IEEE
Southwest Symposium on. IEEE, 2006.
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