utilization of super pixel based microarray image segmentation

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

@ IJTSRD | Unique Paper ID – IJTSRD46274 | Volume – 5 | Issue – 5 | Jul-Aug 2021 Page 2101

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

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: 2456-6470, 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)

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

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.

IJTSRD46274

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470

@ IJTSRD | Unique Paper ID – IJTSRD46274 | Volume – 5 | Issue – 5 | Jul-Aug 2021 102

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

Figure 1: Microarray Image Creation Process

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.

PROPOSED WORK

Noise Removal: � Variational Mode decomposition and Wavelet

based Filtering



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 (Universal Threshold)

36.16 30.98 24.43

VMD+ Wavelet (Sure Shrink) 38.91 32.34 26.98

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.

� Perform Edge detection using Bidimensional Empirical Mode Decomposition

� Perform morphological filling on the edge.

QUALITATIVE ANALYSIS

Figure 4: Spot Detection



SEGMENTATION (SINGLE FEATURE

CLUSTERING ALGORITHMS

� K-means � Moving K-means � Fuzzy C-means

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.

� Based on this observation, multiple feature clustering algorithm is developed for segmentation of microarray images

QUANITATIVE VALUES OF MULTIPLE

FEATURE FCM COMPARED WITH SINGLE

FEATURE K_MEANS AND FCM

Method

MSE Values

Compartment

No 1

MSE Values

Compartment

No 8

K-means 282.781 346.47 Fuzzy c-means

(Single Feature)

216.392 228.69

Multiple feature Fuzzy

C-means 198.327 186.276

TABLE 3: Multiple feature clustering algorithm

MSE Values

SUPER PIXEL BASED FCM SEGMENTATION



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, “Superpixel-Based 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 Sci-ence & 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.

utilization of super pixel based microarray image segmentation

Education

mri

dna

slic

super pixels