IJITCE October 2014

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014

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Vol.4 No.10

August 2014

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From Editor's Desk

Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. The invention of blue light-emitting diodes that are central to the energy-efficient lights illuminating homes, offices and electronic displays has earned three scientists the 2014 Nobel Prize in physics. Isamu Akasaki of Meijo University and Nagoya University in Japan, Hiroshi Amano of Nagoya University and Shuji Nakamura of the University of California, Santa Barbara will split the roughly $1.1 million prize. “If we look at the landscape of technology, there’s the transistor and the integrated circuit, and then there’s the blue LED,” says Fred Schubert, an electrical engineer at the Rensselaer Polytechnic Institute in Troy, N.Y. The blue LED is the crucial ingredient for white LED lamps, which are rapidly replacing incandescent bulbs. Edison’s classic invention uses a filament that emits light in a range of colors that together look white. But a lot of electricity gets wasted heating the filament rather than generating light. LEDs are far more energy efficient because they use electrons to generate photons. LEDs are made out of layers of semiconductors, materials similar to the ones in computer chips. Some layers have an excess of electrons; others have a deficit, leading to the emergence of positively charged holes where electrons. Combine the electrons and holes in a concentrated area and they emit light. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE


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Contents

Image Segmentation & Performance Evaluation Parameters by Nirmal Patel, Rajiv Kumar GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG.GGGGGGGGG.[239]


239

Image Segmentation & Performance Evaluation Parameters

Nirmal Patel Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, India

Gurgaon Institute of Tech. & Mgmt., Gurgaon, India Email: [email protected]

Rajiv Kumar GITM

Gurgaon Institute of Tech. & Mgmt., Gurgaon, India Email: [email protected]

Abstract--- Images are widely used in all walks of life. Image use in daily needs insists upon a robust and result oriented way of analyzing images across all domains. Let it be remote sensing pictures, medical science critical image analysis, biometrics, it has proven to be indispensable to come out with a cut piece of sophisticated algorithms to dispense huge load of image processing requirements. Here we list out some of the effective ways of differentiating image pixels. Image segmentation is the way to carry out segregation of pixels as per desired criteria. Further parameters are figured out to evaluate the performance of these techniques. Keywords— Segmentation, image processing, evaluation parameters, clustering.

I. INTRODUCTION Image is termed as a two-dimensional representation of

pictures consisting of values in numerical form. In digital

form, the image comprises of data premised as pixels at the

lowest level. Pixels are the smallest individual element in an

image, holding finite, discrete, quantized values that represent

the brightness, intensity or gray level at any specific point. [1]

Image processing refers to the analysis of the image and

obtaining desired results. Medical science image

interpretation, remote sensing pictures, face recognition,

pattern matching are among useful applications of image

processing. Image segmentation, morphological operations,

edge detection, image enhancement and restoration are several

operations in image processing. [2]

We have categorized the paper in four sections. First

section brings in light the concept of image segmentation.

Second section presents various segmentation techniques used

these days. Third section describes the performance evaluation

parameters for the segmentation algorithms. Fourthly, the

paper is concluded with a note of revisit.

II. IMAGE SEGMENTATION Image segmentation is a primary process of image analysis

in any situation. It is a process of subdividing an image into

constituents regions or objects so that the minute details of the

image are read or analyzed. [3] Whether one has to classify

stained cells in a tissue or dislocation of constituent areas

depending upon vegetation in an image of a big city taken

from space, image segmentation is the most useful tool

available in MATLAB image processing product.

Fig. 1. An example of image segmentation of an image of a stained tissue (a) original image , (b),(c),(d) are segmented

images

III. SEGMENTATION TECHNIQUES There exist many different types of segmentation

techniques in literature but there is no particular method

which can be applied on different types of images which

would generate same result. Algorithm development for one

class of images may not always be applied to other class of

images [3]. There is a constant challenge to develop a general

segmentation algorithm that could address a large section of

issues concerned with edges, clustering of pixels, region

similarities, pixel classification depending on desired criteria.

Here we present some of the most widely used methods for

segmentation.

A. Thresholding

Thresholding method proposes the use of a threshold value

set by the administrator. If a pixel value lies is equal to or

greater than the threshold value the pixel could be picked up

else left. It implies the picked pixel constitutes the foreground

and the left ones form the background part of the image.[1]

This operation could be classified as local, global and adaptive

application. The local thresholding refers to use of threshold

values over small region. Global thresholding is considered

when intensity variation of object and background is


240

conviniently distinct. Further if the threshold value is

depending upon the spatial co-ordinates, adaptive thresholding

could be used. Histogram shape-based, entropy-based, spatial

methods and local methods are some algorithms based on

thresholding.[4] B. Edge Based

The pixels reflecting abrupt change in intensity are known

as edge pixels. These pixels define different regions in the

image. For detecting these pixels two techniques are used

namely, Gray Histogram Technique and Gradient Based

Method. These methods require a balance between detecting

accuracy and noise immunity in practice. In the former

method, segmentation is done on the basis of a threshold

value. Firstly depending upon the color or intensity a

histogram is calculated from the entire pixel in the image, and

then edges are located on the basis of contours and valleys in

image are located [5]. While in the latter one, convolving

gradient operators with the image is applied. Gradient is

defined as change in magnitude in the image while traversing

from one end to another. If the gradient magnitude is high,

then there is a possibility of rapid transition from one region

to another. Then these are pixels which form edges and

linking of these edges is done to form closed boundaries to

result regions. In these methods commonly used edge

detection operators used in gradient based method are Sobel,

Prewitt and Roberts. Thus, edge detection algorithms are

suitable for images that are simple and noise-free as well often

produce missing edges or extra edges on complex and noisy

images.

C. Region Based Region based segmentation refers to the way of forming

regions in the image based on some seed pixels. Seed pixels

are the main pixels to which other pixels belonging to the

same region must adhere to in terms of gray level within a

certain threshold range.

1.Region Growing: Region growing means starting from

the seed pixels outright towards surrounding ones in order to

include or exclude them on the basis of some membership

condition. These conditions could instantiate as pixel strength,

gray level surface or color. Since the regions are grown up

on the basis of the principle, the image information itself

is significant. For example, if the principle were a pixel

intensity threshold value, information of the histogram of

the image would be of use, as one could use it to

regulate a suitable threshold value for the region membership

condition. The regions need the distance in the entire image

because each point has to fit to one region or another. To

get regions at all, one must define a property that will

be accurate for each region that is defined. The property

selected for the region should be unique in order to

differentiate between any two regions. So that pixels of two

regions don't get mixed and could easily be segregated. If

these principles are met, then the image is correctly

segmented into regions. Region growing segmentation can

take course either of the two - by mean or by max-min

variance. Blocks of 2X2 pixels size could be taken for region

growing mechanism. Taking the max-min instance, the

maximum, minimum intensity variation is taken into

consideration and adjacent regions whose max-min variance is

within an acceptance of the seed blocks or seed pixels are

amalgamated. The new region is now the seed and the process

goes on. Another round of repetition of checking adjacent

regions and comparing them with the acceptable range is

carried out. And thus, region growing segmentation is

implemented across the entire image. While considering the

mean instance, region growing is performed by comparing the

mean values of the blocks with that of the seed blocks.

2. Split and Merge: For the merge and split process blocks

of 16X16 pixels size are taken. If the max-min difference

of a block is near by the max-min difference of its

neighbors, then the blocks are merged . A threshold value is

required for this purpose. This threshold defines which

blocks can be merged into a particular block and which

others could be split into smaller blocks based on the

difference between the maximum and minimum intensities

in every block. A block is split in partial if the max-min

difference of the block outstands the threshold. The

process is pulled over recursively until, no blocks satisfy

the conditions to be split or merged. Thus a block whose

max- min variance exceeds the threshold will continue to

be split until the max-min variance of the subsequent

block(s) are within the threshold. There needs to be the

lower check on upto what extent the blocks to be splitted. In

case this is specified the algorithm remains consistent and

doesn't take a chance to retire into unwanted situation

producing awkward results. This could be done by specifying

the smallest block dimensions that could be generated through

splitting. D. Clustering

Clustering is the way of grouping a set of objects in such a

way that objects in the same group called a cluster, are more

similar in some sense or another to each other than to those in

other groups or clusters.

A cluster is therefore a collection of objects which are

“similar” between them and are “dissimilar” to the objects

belonging to other clusters. An image can be grouped based

on keyword (metadata) or its content (description). A variety

of clustering techniques have been introduced to make the

segmentation more effective.

1) K-Means Clustering: In K-means algorithm data

vectors are grouped into predefined number of clusters. At the

beginning the centroids of the predefined clusters is initialized

randomly. The dimensions of the centroids are same as the

dimension of the data vectors. Each pixel is assigned to the

cluster based on the closeness, which is determined by the

Euclidian distance measure. After all the pixels are clustered,

the mean of each cluster is recalculated. This process is

repeated until no significant changes result for each cluster

mean or for some fixed number of iterations. [9].

Steps: • K clusters are formed by partitioning the dataset and the

data points are randomly assigned to the clusters

resulting in clusters that have roughly the same number

of data points.

• For all data point the distance from the data point to

each cluster is calculated.

• Leave the data point where it is only if it closes to its

own cluster. If the data point is not close to its own

cluster, shift it into the closest cluster.


241

• Repeat the above step until a complete pass through all

the data points‘ results in no data point moving from

one cluster to another cluster. On this point the clusters

are stable and the clustering process ends.

2) Fuzzy C-Means Clustering: The fuzzy clustering method is devised to confront the real situations when some

issues may erupt due to partial spatial resolution, intensity of

overlapping, poor contrast, noise and intensity in

homogeneities. Taking into consideration the two main

clustering strategies - the hard and the fuzzy clustering, fuzzy

clustering scheme is a soft segmentation method and has been

generally studied and successfully applied in image clustering

and segmentation. Due to robust characteristics for ambiguity

and can retain much more information than hard segmentation

methods. Fuzzy c-means (FCM) algorithm is most popularly

used than other fuzzy clustering techniques[10].

Steps: • Set values for c, m and e. Where 'c' is number of

clusters, 'm' is fuzzy factor and 'e' is stopping condition.

• Do initialization of fuzzy partition matrix.

• Set the loop counter b.

• Calculate the c cluster centers.

• Calculate the membership matrix.

• Set b= b+1 and go to step 4.

Fig.2.Image segmentation methods

IV. EVALUATION PARAMETERS Having segmentation evaluation measures is an efficient

way to analyze the performance of existing and future

algorithms. Segmentation evaluation metrics can be divided

into boundary –based and region –based methods. Before one

gets to know the performance of an algorithm, knowing

comprehensively the definitions of these metrices is

inevitable. Various performance parameters used for

evaluation of image segmentation are as follows.

E. The Rand index (RI)

Rand index counts the fraction of pairs of pixels who’s

labeling are consistent between the computed segmentation

and the ground truth averaging across multiple ground truth

segmentation [6]. The Rand index or Rand measure is a

measure of the similarity between two data clusters. Given a

set of n elements and two partitions of S to compare, we

define the following(a), the number of pairs of elements in S

that are in the same set in X and in the same set in Y. (b), the

number of pairs of elements in S that are in different sets in X

and in different sets in Y. (c), the number of pairs of elements

in S that are in the same set in X and in d ifferent sets in Y.(

d), the number of pairs of elements in S that are in different

sets in X and in the same set in Y The Rand index I is,

Where, a + b is the number of agreements between X and Y

and c + d is the number of disagreements between X and Y.

The Rand index has a value between 0 and 1, with 0

indicating that the two data clusters do not agree on any pair

of points and 1 indicating that the data clusters are exactly the

same.

F. Variation of Information (VOI)) The Variation of Information (VOI) metric defines the

distance between two segmentations as average conditional

entropy of one segmentation given the other, and thus

measures the amount of randomness in one segmentation

which cannot be explained by the other [6]. Suppose we have

two clustering (a division of a set into several subsets) X and

Y where is:

Then the variation of information between two clustering

Where, H(X) is entropy of X and I(X, Y) is mutual

information between X and Y. The mutual information of two

clustering is the loss of uncertainty of one clustering if the

other is given. Thus, mutual information is positive and

bounded by

G. Global Consistency Error (GCE))

The Global Consistency Error (GCE) measures the extent

to which one segmentation can be viewed as a refinement of

the other [8]. Segmentations which are related are considered

to be consistent, since they could represent the same image

segmented at different scales. Segmentation is simply a

division of the pixels of an image into sets. The segments are

sets of pixels. If one segment is a proper subset of the other,

then the pixel lies in an area of refinement, and the error

should be zero. If there is no Subset relationship, then the two

regions overlaps in an inconsistent manner. The formula for

GCE is as follows,

Where, segmentation error measure takes two

segmentations S1 and S2 as input, and produces a real valued

output in the range [0::1] where zero signifies no error. For a

given pixel pi consider the segments in S1 and S2 that contain

that pixel.

It measures the extent to which one segmentation can be

viewed as a refinement of the other. Segmentations which are

related in this manner are considered to be consistent, since

they could represent the same natural image segmented at

different scales.


242

H. Boundary Displacement Error (BDE)

The Boundary Displacement Error (BDE) measures the

average displacement error of one boundary pixels and the

closest boundary pixels in the other segmentation [7].

Particularly, it defines the error of one boundary pixel as the

distance between the pixel and the closest pixel in the other

boundary image.

I. Mean absolute error (MAE)

Mean absolute error is the average of the difference

between predicted and actual value in all test cases; it is the

average prediction error. MAE indicates that higher the values

of MAE mean the image is of poor quality. Mean absolute

error (MAE) is a quantity used to measure how close forecasts

or predictions are to the eventual outcomes.

J. Peak signal to noise ratio (PSNR)

It gives quality of image in decibels (db).and is given as

Fig. 2. Various Segmentation Parameters

V. CONCLUSION Towards the end we want to recollect that we stated the

concept of image segmentation. We drove through various

segmentation methods prevailing these days. Also we

concluded with the performance parameters useful in judging

an algorithm's utility. A useful lay of knowledge lastly is

hopeful in extending the reader's repository of intellect.

ACKNOWLEDGMENT Authors wish to thank the MATLAB workshop team for

organising a spectacular event at the MD University, Rohtak,

India.

REFERENCES [1]. P. Rafael C. Gonzalez and Richard E. Woods, “Digital

Image Processing”, Third Edition, Pearson Education, Asia.

[2].http://www.mathworks.in/help/pdf_doc/images/images_tb.

pdf

[3].Jay Acharya, Sohil Gadhiya and Kapil Raviya,

“Segmentation Techniques for Image Analysis: A Review”,

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[6].Vijay Kumar Chinnadurai, Gharpure Damayanti

Chandrashekhar,” Improvised levelset method for

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[7]. S.L.A Lee, A.Z.Kouzani, E.J.Hu,” Empirical Evaluation

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[8]. Allan Hanbury, Julian Stottinger,” On segmentation

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[9]. Monika Xess and S.Akila Agnes”, Survey On Clustering

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[10] Yong Yang,Image Segmentation by Fuzzy C-Means

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and Economics Nanchang 330013, P.R. China.

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