Performance analysis of image filtering technique and combined approach for image segmentation

7/23/2019 Performance analysis of image filtering technique and combined approach for image segmentation

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International Journal of Advanced Engineering Research and Technology (IJAER

Volume 2 Issue 8, November 2014, ISSN No.: 2348 – 81

www.ijaert.org

Performance Analysis of Image Filtering Technique and Combined

Approach for Image Segmentation

Shradha P. Dakhare*, Manoj B. Chandak**

*Department of Computer Science & Engineering, Nagpur UniversityW.C.E.M. Dongargaon, Nagpur, India

**Department of Computer Science & Engineering, Nagpur University

S.R.C.O.E.M. Nagpur, India

ABSTRACTSegmentation of an image entails the division orseparation of the image into regions of similar attribute.The accurate and effective algorithm for segmenting

image is very useful in many fields, Many imagesegmentation techniques have been developed over the

past two decades for segmenting the images, which helpfor object recognition, occlusion boundary estimationwithin motion or stereo systems, image compression,

image editing.In this project that is combined approach for segmentingthe image, first we used histogram equalization to theinput image, from which we get contrast enhancement

output image. After that we applied median filtering whichwill remove noise from contrast output image. At last weapplied fuzzy c-mean clustering algorithm to denoising

output image, which give segmented output image.

Keywords - Histogram Equalization(HE), Median Filter(MF), Fuzzy C Means(FCM).

I. INTRODUCTION

Image segmentation refers to the major step in image

Processing in which the inputs are images and, outputs arethe attributes extracted from those images. The goal ofsegmentation is typically to locate certain objects ofinterest which may be depicted in the image. It is one ofthe challenging tasks in image analysis.

Segmentation is the first essential and important step oflow level vision [9]. There are many application of imagesegmentation. For example, in a vision guided car

assembly system, the Robot needs to pick up theappropriate components from the bin .For thissegmentation followed by recognition is required. Itsapplication area varies from the detection of cancerouscells to the identification of an airport from remote sensing

data, etc. In all this area, the quality of final outputdepends largely on the quality of segmented output.

Segmentation is the process of partitioning an image innon-intersecting regions such that each region homogeneous and the union of no two adjacent regions

homogeneous. Formally, it can be defined as follows:If F be the set of all pixels and P() be a uniformi

(homogeneity)predicate defined on groups of connect pixels, then segmentation is a partitioning of the set F ina set of connected subsets or regions(S 1 , S 2 , · · · , S n) su

that

The uniformity predicate for all regio

and when is adjacent to

Note that this definition is applicable to all type of Image

Clustering is powerful technique that has been research image segmentation. It is the process of organizing daobjects into a set of disjoint classes called clusterClustering aims to analyze and organize data into grou

based on their similarity [6]. Clustering is an example unsupervised classification. This paper is divided in section, in first section for each input image, HE , MF anFCM are applied and compare their output on the basis time, mean square error (MSE), peak signal noise rat(PSNR), figure of merit (FOM) and data loss. In secon

section HE & FCM, HE & MF and MF & FCM acombined and compare there output. In third, combiapproach. for one input image HE is applied and toutput get from this HE, to the same output MF is appliand the output get from MF ,to the same output FCM

applied . The output get from this is the better segment

output.

II. HISTOGRAM EQUALIZATION

Histogram Equalization is a method that increases th

contrast of an image by increasing the dynamic range intensity given to pixels with the most probable intensi

values. One transformation function that accomplishes this a cumulative distribution function. In the histograequalization function that was used to produce the resu

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shown in this report, the transformation is scaled such that

the least intense value in the original image is mapped to azero intensity value in the equalized image. As well, themost intense value in the original image is mapped to anintensity value that is equal to the maximum intensity

value determined by the bit depth of the image.

a)Normal HE b)Adaptive HE

Fig. 2.1 This is an image of brain Tumor. Histogramequalization increases the contrast of image in the image

a) is normal Histogram Equalization and b)is Adaptive

Histogram Equalization

2.1

ADAPTIVE HISTOGRAM EQUALIZATION

AHE is a computer image processing technique used toimprove contrast in images. It differs from ordinaryhistogram equalization in the respect that the adaptive

method computes several histograms, each correspondingto a distinct section of the image, and uses them to

redistribute the lightness values of the image. It istherefore suitable for improving the local contrast of animage and bringing out more detail. However, AHE has a

tendency to over amplify noise in relatively homogeneousregions of an image. Ordinary histogram equalization uses

the same transformation derived from the image histogramto transform all pixels. This works well when the

distribution of pixel values is similar throughout the image[10].

III. MEDIAN FILTER

Median filtering is one kind of smoothing technique, as linear Gaussian filtering. All smoothing techniques aeffective at removing noise in smooth patches or smooregions of a signal, but adversely affect edges. Oft

though, at the same time as reducing the noise in a signait is important to preserve the edges. Edges are of criticimportance to the visual appearance of images. For smato moderate levels of (Gaussian) noise, the median filter demonstrably better than Gaussian blur at removing noiwhilst preserving edges for a given, fixed window siz

However, its performance is not that much better thGaussian blur for high levels of noise, whereas, fspeckle noise and salt and pepper noise (impulsive noise

it is particularly effective. Because of this, median filterinis very widely used in digital image processing.

Fig. 3.1 Original Image is image of brain tumor havinnoise and Filter image is noise removed image by Media

Filter.

IV. FUZZY C MEANS

In fuzzy clustering (also referred to as soft clusteringdata elements can belong to more than one cluster, an

associated with each element is a set of membershlevels. These indicate the strength of the associatio

between that data element and a particular cluster [4

Fuzzy clustering is a process of assigning themembership levels, and then using them to assign da

elements to one or more clusters. One of the most wideused fuzzy clustering algorithms is the Fuzzy C-Mea

(FCM) Algorithm (Bezdek 1981).In the FCM approach, the same given datum does n belong exclusively to a well-defined cluster, but it can

placed in a middle way as shown in the diagram below. this case, the membership function follows a smoother lito indicate that every datum may belong to several clustewith different values of the membership coefficient [5].



http://en.wikipedia.org/wiki/Image_processing

http://en.wikipedia.org/wiki/Contrast_%28vision%29

http://en.wikipedia.org/wiki/Histogram_equalization

http://en.wikipedia.org/wiki/Histogram

http://en.wikipedia.org/wiki/Signal_noise

http://en.wikipedia.org/wiki/Signal_noise

http://en.wikipedia.org/wiki/Histogram

http://en.wikipedia.org/wiki/Histogram_equalization

http://en.wikipedia.org/wiki/Contrast_%28vision%29

http://en.wikipedia.org/wiki/Image_processing






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Fig. 4.1 Membership function of fuzzy clustering

4.1. ALGORITHM OF FUZZY C MEANCLUSTERING

Step1. Choose a number of clusters in a given image.Step2. Assign randomly to each point coefficients for

being in a cluster.Step3. Repeat until convergence criterion is met.

Step4. Compute the center of each cluster.Step5.For each point, compute its coefficients of being inthe cluster [4-5].

Fig.4.2 Shows the image segmented by C means algorithm

V. COMBINED APPROACH

In this for one input image HE is applied and the outputget from this HE, to the same output MF is applied and theoutput get from MF, to the same output FCM is applied.

The output get from this is the better segmented output.

Histogram EqualizationInput Image

Contrast

Output

image

Median Filter

Fuzzy C-means

Noise

removed

Output image

Segmented

Output image

Fig. 5.1 Working of combined approach

Figure 5.2: show the image formed by combin

Approach

Fig. 5.2 this shows, combine output of all techniques.

5.1. RESULT

By combining HE & FCM, data loss is improved.

By combining HE and MF none of the factor improved








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By combining MF & FCM none of the factor is

improved.

By combined approach accuracy is improved.

VI.

CONCLUSIONThe techniques are developed in MATLAB lab for

analysis and comparisons. Combined approach produces

fairly higher accuracy as compare to FCM. The future

scope of combined approach is to decrease the

computation time and increase a PSNR, which generally

indicates that the reconstruction is of higher quality.

REFERENCES

Journal Papers:

[1]

M. M. Abdelsamea ,“An Enhancement Nieghborhoodconnected segmentation for 2D cellular Image”,

International Journal of Bioscience, Biochemistry andBioinformatics, Vol. 1, No. 4, November 2011.

[2] Gunwanti S. Mahajan, Kanchan S. Bhagat, “Medicalimage segmentation using enhanced k-means andkernelized fuzzy c- means ”, (ijecet) Volume 4, Issue 6,

November – December, 2013.[3] Rupinder Singh, Jarnail Singh, Preetkamal Sharma,

Sudhir Sharma, ---Edge based region growing,rupinderSingh et al, Int. J. Comp. Tech. Appl., Vol 2 (4), 1122-

1126 IJCTA | JULY-AUGUST 2011.

[4]

Mrs. Bharati R.Jipkate, Dr. Mrs.V.V.Gohokar, “AComparative Analysis of Fuzzy C-Means Clustering

and K Means Clustering Algorithms”, IJCER , May-June 2012 , Vol. 2 .Issue No.3, ISSN: 2250 – 3005 .

[5] B.Sathya, R.Manavalan, “Image Segmentation byClustering Methods:performance Analysis”,International Journal of Computer Applications (0975

– 8887) Volume 29 – No.11, September 2011.[6] Jian-Ping Mei and Lihui Chen “An Enhanced Fuzzy c-

Means Clusterings using Relational Information”,©2011 IEEE.

[7] Bei Yan, Mei Xie, Jing-Jing Gao, Weizhao ,“A fuzzy

c-means based algorithm for bias field estimation andsegmentation of MR images ”, ©2010 IEEE.

[8] R. Krishnapuram and J. Keller, “A possibilisticapproach to clustering”, IEEE Trans. Fuzzy Syst., vol.1, no. 2, pp. 98 – 110, Apr. 1993.

[9] Nikhil R.Pal and Sankar K. Pal, “Review on ImageSegmentation Techniques” , vol 26 March 1993.

[10] Stephen M.Pizer, E. Philip Amburn, John D.Austin, Robert Cromartie, John B. Zimmerman and

Karel Zuiderveld ,“Adaptive Histogram Equalizatio

and its variation”,© 1987 by Academic press.

Books:

[11]

Gonzalez, Woods, and Eddins, Digital Ima

Processing Using MATLAB, Prentice Hall 2004. (C3, Ch. 5).




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Performance analysis of image filtering technique and combined approach for image segmentation