International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 5 Issue 5, July-August 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
Utilization of Super Pixel Based Microarray Image Segmentation
Mr. Davu Manikanta, Mr. Parasurama N, K Keerthi
Department of ECE, JNTUK University College, PPDCET Vijayawada, Andhra Pradesh, India
ABSTRACT
In the division of PC vision pictures, Super pixels are go probably as
key part from 10 years prior. There are various counts and
methodology to separate the Super pixels anyway whole all of them
the best super pixel looking at strategy is Simple Linear Iterative
Clustering (SLIC) have come to pivot continuously recently. The
concentrating of small scale group quality verbalization from MRI
imaging is more useful to perceive tumors or some other dangerous
development contaminations, so the fundamental DNA (cDNA)
microarray is a grounded device for analyzing the same.
The division of microarray pictures is the essential development in a
microarray assessment. In this paper, we proposed a figuring to
dividing the cDNA small show picture using Simple Linear Iterative
Clustering (SLIC) based Self Organizing Maps (SOM) method. In
any case, the proposed figuring is taken up a moving task to look at
the bad quality of pictures in addition. There are two phases to
separate the image, introductory, a pre-setting up the applied picture
to diminish fuss levels and second, to piece the image using SLIC
based SOM approach.
KEYWORDS: MRI, DNA, SLIC, Super pixels, SOM, Quality
clarification microarray
I.
INTRODUCTION
A super pixel is a heap of basic proximal and
homogeneous pixels. The homogeneous would be
portrayed similar to measure, significance of
concealing, significance of surface, etc The Super
pixels are known to save the close by picture credits,
for instance, object cutoff points, shape and area, and
decrease the cost of assessment of various PC vision
division issues. Along these lines, super pixel over
division capably decreases the no. of units to manage
an image. The SOM approach is best anytime to think
about the image pixels and here we are introducing
another system of microarray picture division using
SLIC over SOM. The critical central focuses for
using this advancement are; to handle features with
more significant regions and to diminish the data
objects for the sub-sequent estimations.
Quality clarification microarray (GEM) tests amass
fundamental trademark data gathering regular
information from tests like tissues, cell lines recorded
GEM information hold quality data over all models in
the insight. As a matter of fact incalculable qualities
is assessed and recorded simultaneously. In different
viewpoints these models can be diverse under
How to cite this paper: Mr. Davu
Manikanta | Mr. Parasurama N | K
Keerthi "Utilization of Super Pixel
Based Microarray Image Segmentation"
Published
in
International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456IJTSRD46274
6470, Volume-5 |
Issue-5,
August
2021,
pp.2101-2105,
URL:
www.ijtsrd.com/papers/ijtsrd46274.pdf
Copyright © 2021 by author (s) and
International Journal of Trend in
Scientific Research and Development
Journal. This is an
Open Access article
distributed under the
terms of the Creative Commons
Attribution License (CC BY 4.0)
(http://creativecommons.org/licenses/by/4.0)
acumen. To locate the basic attributes for a specific
goal is a basic district of assessment. These attributes
are called illuminating qualities. The divulgence of
enlightening attributes is essential to the expert for
judge a patients and for the affiliation that are making
drugs over the a few years, a ton of exertion has been
set in the improvement of answer for the informative
qualities openness. Till now the undertaking is
inconceivably trying and some phenomenal strategies
are set up to beat the standard Approaches. The
microarray picture assessment.
Feature confirmation when utilized for microarray
quality clarification information is called quality
choice. Beside scrutinize of dimensionality there are
different issues looked in quality affirmation like
mislabeled information, dull information, pointless
and crazy information, and issue of cross-stage
appraisals, wrong and tendency issue and burden in
typical data recovery. Particular quality choice
strategies and assessments are proposed recorded as a
printed copy which can diminish the dimensionality
by removing unessential, tedious and clamorous
attributes.
@ IJTSRD | Unique Paper ID – IJTSRD46274 | Volume – 5 | Issue – 5 | Jul-Aug 2021
Page 2101
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
1.1. Related work
Basic direct iterative social affair (SLIC) is an
embrace of means for Super pixels age with two
colossal partitions like the no. of distance calculations
in the overhaul is profoundly diminished by the
multifaceted nature to be straight in the measure of
pixels N and independent of the no. of Super pixels k
and the other one is a weighted distance survey
consolidate the tone and spatial proximity while
simultaneously giving request over the size of pixel
and strength of the Super pixels.” The “section of
camouflaging picture has been wound up being
irritating considering the way that it combines a
goliath degree of data arranging. Regardless of the
way that stunning undertakings have been given to, a
few issues are currently not totally tended to. Dong
and Xin master watched out for a blend system which
circuits solo division and encouraged division. The
free division is refined by a two-level methodology,
i.e., lesser covering and concealing get-together. The
managed division joins covering learning and pixel
gathering. Reenacted propping (SA) has been used for
finding the ideal packs shape SOM models". The
calculation of SLIC Super pixels age is given under.
In the past, scientists have only been able to conduct
these genetic analyses on a few genes at once. With
the development of DNA microarray technology,
however, scientists can now examine how active
thousands of genes are at any given time.
The functional organization of gene cell has been
investigated by the developed tools in large scale
analysis leads to the success of developing many
techniques in bioengineering. Out of these techniques,
DNA microarray technology has developed more
powerful by enabling the biologists to investigate
thousands of DNA genes concurrently that lies in the
entire organism.
2. CREATION OF MICROARRAY IMAGE
EXISTING NOISE REMOVAL METHODS
Mean Filter
Median Filter
Vector Filtering techniques
Vector Median Filter
Vector directional filter
Directional Distance Filter
Center weighted Vector median filter
Adaptive Center Weighted Vector Median Filter
Wavelets based filtering
Empirical Mode Decomposition
DISADVANTAGES OF EXISTING WORK
1. Noise Removal
Highly depending on the intensity characteristics
of the image.
2. Segmentation
1. The need of human intervention and correct the
potential alignment and rotation problems.
2. Sensitive to contaminations and large number of
missing spots.
3. Parameters about the sub-array and spots are
required ie., number of spots in each row and
column.
4. Segmentation using clustering algorithm is carried
out using single feature.
5. No post processing is required.
PROBLEM STATEMENT
NOISE REMOVAL:
If we use VMD+DWT Technique then we can
perform Computationally efficient, Rotationally
symmetric(perform the same in all directions)
SEGMENTATION
Clustering Algorithms are not restricted to a
particular spot size and shape, does not require an
initial state of pixels and no need of post
processing.
MULTIPLE
FEATURE
CLUSTERING
SEGMENTATION
In the microarray image segmentation problem,
not only the pixel intensity, but also the distance
of pixel from the center of the spot and median of
intensity of a certain number of surrounding pixels
influences the result of clustering.
Based on this observation, in this research,
multiple feature fuzzy c-means clustering
algorithm is proposed, which utilizes more than
one feature.
Figure 1: Microarray Image Creation Process
PROPOSED WORK
Noise Removal:
Variational Mode decomposition and Wavelet
based Filtering
@ IJTSRD | Unique Paper ID – IJTSRD46274 | Volume – 5 | Issue – 5 | Jul-Aug 2021
Page 2102
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
Segmentation:
Multiple feature clustering
Super pixel-based segmentation methods
NOISE REMOVAL ALGORITHM USING VMD
WAVELETC
Figure 2: Noise removal using VMD + Wavelet
FLOW DIAGRAM OF FILTERING USING
VMD+ WAVELET
VMD: Variational mode decomposition VMF:
Vector Median Filter
NOISE REMOVAL USING VMD-DWT
METHOD
PSNR(Peak signal to noise ratio)
PSNR, is the ratio between the maximum possible
power of a signal and the power of corrupting noise
Here, MAXf is the maximum possible pixel value of the image
PSNR(Peak signal to noise ratio) VALUES OF FILTERING METHOD
The value of σ denotes the standard deviation value used in imnoise() function in matlab to add
Gaussian noise to microarray image
Method
σ = 0.015
σ = 0.025
σ = 0.036
Wavelet(Universal Threshold)
22.02
16.98
14.89
Wavelet(SURE shrink)
21.90
16.11
13.76
BEMD (Universal Threshold)
34.11
28.86
22.86
BEMD (SURE shrink)
35.89
29.74
23.11
VMD+ Wavelet
36.16
30.98
24.43
(Universal Threshold)
VMD+ Wavelet (Sure Shrink)
38.91
32.34
26.98
QUALITATIVE ANALYSIS
Perform Edge detection using Bidimensional
Empirical Mode Decomposition
Perform morphological filling on the edge.
QUALITATIVE ANALYSIS
Fig3 :Sub-gridding
AUTOMATIC
SPOT
DETECTION
ALGORITHM
The steps of the automatic spot detection algorithm
are as follows:
Consider the sub-gridded microarray image.
Figure 4: Spot Detection
@ IJTSRD | Unique Paper ID – IJTSRD46274 | Volume – 5 | Issue – 5 | Jul-Aug 2021
Page 2103
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
SEGMENTATION
(SINGLE
CLUSTERING ALGORITHMS
K-means
Moving K-means
Fuzzy C-means
FEATURE
QUANTITATIVE VALUES OF SINGLE
FEATURE CLUSTERING ALGORITHMS
SEGMENTATION
USING
MULTIPLE
FEATURE CLUSTERING ALGORITHM
In microarray image segmentation, the position of
the pixel and median value of surrounding pixels
also influences the result of clustering and
subsequently that leads to segmentation.
Based on this observation, multiple feature
clustering algorithm is developed for segmentation
of microarray images
SEGMENTATION
USING
MULTIPLE
FEATURE CLUSTERING ALGORITHM
In microarray image segmentation, the position of
the pixel and median value of surrounding pixels
also influences the result of clustering and
subsequently that leads to segmentation.
QUANITATIVE VALUES OF MULTIPLE
FEATURE FCM COMPARED WITH SINGLE
FEATURE K_MEANS AND FCM
MSE Values
MSE Values
Method
Compartment Compartment
No 1
No 8
K-means
282.781
346.47
Fuzzy c-means
(Single
216.392
228.69
Feature)
Multiple
feature Fuzzy
198.327
186.276
C-means
TABLE 3: Multiple feature clustering algorithm
MSE Values
SUPER PIXEL BASED FCM SEGMENTATION
Based on this observation, multiple feature
clustering algorithm is developed for segmentation
of microarray images
@ IJTSRD | Unique Paper ID – IJTSRD46274 | Volume – 5 | Issue – 5 | Jul-Aug 2021
Page 2104
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
CONCLUSION
The microarray image analysis was done in three
stages: Noise reduction,, segmentation and intensity
extraction. Existing microarray analysis mechanisms
are semi-automatic in nature, requiring human
intervention, initialization of parameters etc. In this
work we are developed a truly automatic system for
microarray image analysis.
New algorithms are developed at each and every stage
of microarray analysis.
In this research, a new
algorithm for noise removal in microarray image is
developed without effecting the edge information of
spot. For segmentation, algorithms based on multiple
feature clustering and super pixels based segmentation
are developed.
REFERENCES
[1] Durga Prasad Kondisetty, Dr. Mohammed Ali
Hussain, “A Robust Method for Reducing
Image Noise in Microarray Images International
Journal of Pure and Applied Mathematics,
Volume 117 No. 19 2017, 441-447.
[2]
[Durga Prasad Kondisetty, Dr. Mohammed Ali
Hussain, “ Gene Microarray Analysis using Self
Organizing Maps” Interna-tional Journal of
Pure and Applied Mathematics, Volume 117
No. 19 2017, 441-447.
[3]
Xudong Jin and Yanfeng Gu, “SuperpixelBased Intrinsic Image Decomposition of
Hyperspectral Images” IEEE Transactions on
Geoscience and Remote Sensing, vol. 55, no. 8,
August
2017.
https://doi.org/10.1109/TGRS.2017.2690445.
[4]
Guifang Shao, Tingna Wang, Wupeng Hong,
Zhigang Chen, “An Improved SVM Method for
eDNA Mieroarray Image Segmentation”. The
8th International Conference on Computer Science & Education (ICCSE 2013) April 26-28,
2013. Colombo, Sri Lanka.
[5]
Natarajan P, Sushmitha G, “Brain Tumor
Detection in MRI Brain Images using Threshold
Operation” International Journal of Pharmacy &
Technology, ISSN: 0975-766X, August-2016.
[6]
Stamos Katsigiannis, Eleni Zacharia, Dimitris
Maroulis, “Grow-Cut Based Automatic cDNA
Microarray Image Segmenta-tion” IEEE
Transactions on Nanobioscience, vol. 14, no. 1,
January
2015.
https://doi.org/10.1109/TNB.2014.2369961.
[7]
Rajendra Nagar, Shanmuganathan Raman,
“SymmSLIC Symmetry Aware Superpixel
Segmentation” The IEEE Internation-al
Conference on Computer Vision (ICCV), 2017,
pp. 1764-1773. https://doi.org/10.1109/ICCVW.
2017.208.
[8]
S. Shirakawa, T. Nagao. “Evolutionary image
segmentation based on multi objective
clustering”. In Proceedings of IEEE Con-gress
on
Evolutionary Computation,
IEEE,
Trondheim, Norway pp. 2466–2473, 2009.
[9]
Rezaul Karim, Md. Khaliluzzaman, Sohel
Mahmud, “A Review of Image Analysis
Techniques for Gene Spot Identification in
cDNA Microarray Images” Next Generation
Information Tech-nology (ICNIT), 2011 The
2nd International Conference.
[10]
Maziidah Mukhtar Ahmad, Asral Bahari
Jambek, Mohd Yusoff bin Mashor, “A Study on
Microarray Image Gridding Tech-niques for
DNA Analysis” 2nd International conference on
electric design (ICED) August 19-21 2014,
Malaysia. [11] Revathi T, Sathish A, Sumathi P,
“Microarray Analysis us-ing Fuzzy C-Means
Clustering Algorithm”. International Journal of
Innovative Research in Electrical, Electronics,
Instrumentation and Control Engineering Vol.
2, Issue 1, Jan 2014.
[11]
David Moena Q, “Microarray Image Gridding
by using Self-Organizing” IEEE transactions
978-1-4244-5089- 2011. [13] R. M. Farouk, M.
A. SayedElahl, “Robust cDNA microar-ray
image segmentation and analysis technique
based on Hough circle transform” International
Journal of Pure and Applied Mathematics,
Volume 117 No. 19 2017, 441-447.
@ IJTSRD | Unique Paper ID – IJTSRD46274 | Volume – 5 | Issue – 5 | Jul-Aug 2021
Page 2105