Chapter 3 Methdology

8/2/2019 Chapter 3 Methdology

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Chapter 3

PROPOSED METHODOLOGY FOR SEGMENTATION

3.1 INTRODUCTION

In the previous chapter we discuss the overview of different segmentation technique.

As there are various algorithms and methods are available for segmentation. In this

chapter the main terminologies which are used in this approach are discuss.

Segmentation of an image into foreground and background has been done in much

literature by means of intensity thresholding. However selecting the threshold which

will work for different kind of images is very challenging task. A rigid or fix

threshold may perform very well in some cases but may fail completely in other

cases. Therefore there is a need of adaptive threshold selection.

There are different approaches of adaptive threshold selection: threshold selection

based on mean value, threshold selection based on median value. In this approach

threshold value is selected by using fuzzy methodology. This work discusses the fact

that in an output image most of the pixels belongs to background and very few of the

pixels belongs to the objects present in that which corresponds to foreground. In this

project, the foreground is assumed as an outlier and experimentally it shows the

outlier is detected. The Generalize block diagram of this approach is as shown in

figure 3.1.


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Figure 3.1 Generalized Block Diagram

3.2 PREPROCESSING

Preprocessing of an image is done by different methods depending upon the type of

an image or the algorithm which is going too used in the particular type of

segmentation. Mainly preprocessing is get classified into following types,

3.2.1 Data processing

The images are subject to various types of noises such as irregularities etc. These

noises may degrade the quality of the image and consequently it cannot provide

correct information for subsequent image segmentation and edge detection. In order to

improve the quality of the image, operations need to be performed to remove or

decrease degradations suffered in its acquisition. Preprocessing is also needed in order

to homogenize and separate the intensity distributions of the malignant and benign

tissues. This can be achieved by using several denoising techniques, viz., Gaussian

filter, median filter, Weiner filter, morphological open-close reconstruction filter and

morphological top hat filtering.

Preprocessing FindingThreshold

Foreground &

Background

Separation Filtering Segmentation

Morphological

Erosion and

Dilation


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3.2.2 Image smoothing

Image smoothing act as the pre-processing step for image segmentation, as, almost all

of the images suffers from the problem of noise effects. So, pre-processing act as an

important aid to the every already existing segmentation methods, in which

specialized filters as described above smooth the image and simplifying it for

segmentation step.

3.2.3 Image contrast enhancement

Poor contrast is usually one of the most common defects found in the acquired image.

This degradation probably is caused by inadequate aperture size and noise. Sometimes

this is caused of nonlinear mapping of the image intensity. The effect of such defects

has a great impact on the contrast of the acquired image. In this case, the gray level of

each pixel is scaled to improve the contrast of the acquired image. Contrast

enhancement step sometimes proves to be one of the important pre-processing steps,

especially in case when image has a poor contrast. In the present work, the contrast of

the smoothened image is enhanced using the image processing toolbox functions. This

improves the visualization of the original image and thus makes the object of interest

more clearly visible.

In this approach preprocessing is done by Histogram Normalization on gray scale

image.

3.2.4 Histogram normalization

A normalized histogram gives the relative proportions of each pixel in the image and

hence approximates to the probability distribution of pixel intensities. A normalized

histogram is one in which the sum of the frequencies is exactly 1. Therefore, if you

express each frequency as a percentage of the total, you get a normalized histogram.


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The histogram of digital image with the intensity levels in the range [0, L-1] is a

discrete function.

= 3.1

Where

is the intensity value.

is the number of pixels in the image with intensity .

is the histogram of the digital image with Gray Level

Histograms are frequently normalized by the total number of pixels in the image.

Assuming a M N image, a normalized histogram.

= , K=0, 1, 2, 3.L -1 3.2

Where

gives an estimate of the probability of occurrence of gray level

The Sum of all components of a normalized histogram is equal to1.

A histogram is created by counting the number of pixels at each intensity level

between 0 and 255, so a one-dimensional array of 256 elements is required. The

histogram is normalized by dividing the frequency at each intensity level by the total

number of pixels in the image. A cumulative histogram is created by summing the

frequencies up to and including the required value. A 'for' loop traverses the

histogram array adding each element to the subsequent element to achieve this. If the

intensity range of the image is 50 to 180 and the desired range is 0 to 255 the process

entails subtracting 50 from each of pixel intensity, making the range 0 to 130. Then

each pixel intensity level is multiplied by 255/130, making the range 0 to 255. Auto-

normalization in image processing software typically normalizes to the full dynamic

range of the number system specified in the image file format. The normalization


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process will produce iris regions, which have the same constant dimensions, so that

two photographs of the same iris under different conditions will have characteristic

features at the same spatial location.

Following figure shows the un-normalized histogram and after applying normalized

histogram calculation we get normalized histogram.

Figure 3.2 Histogram Normalization

3.3 THRESHOLDING

Image thresholding is widely used as a popular tool in image segmentation. It is

useful to separate objects from background, or discriminate objects from objects that

have distinct grey levels. Thresholding involves bi-level thresholding and multilevel

thresholding. Bi-level thresholding classifies the pixels into two groups, one including

those pixels with grey levels above a certain threshold, the other including the rest.

Multilevel thresholding divides the pixels into several classes. During the

thresholding process, individual pixel in an image are marked as "object" pixels if


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their value is greater than some threshold value (assuming an object to be brighter

than the background) and as "background" pixels otherwise. This convention is

known as threshold above. Variants include threshold below, which is opposite of

threshold above; threshold inside, where a pixel is labeled "object" if its value is

between two thresholds; and threshold outside, which is the opposite of threshold

inside. Typically, an object pixel is given a value of 1 while a background pixel is

given a value of 0. Finally, a binary image is created by coloring each pixel white or

black, depending on a pixel's labels.

3.3.1 Threshold selection

The key parameter in the thresholding process is the choice of the threshold value (or

values, as mentioned earlier). Several different methods for choosing a threshold

exist. Users can manually choose a threshold value, or a thresholding algorithm can

compute a value automatically, which is known as automatic thresholding. A simple

method would be to choose the mean or median value, if the object pixels are brighter

than the background, they should also be brighter than the average. In a noiseless

image with uniform background and object values, the mean or median will work well

as the threshold, however, this will generally not be the case. A more sophisticated

approach might be to create a histogram of the image pixel intensities and use the

valley point as the threshold. The histogram approach assumes that there is some

average value for the background and object pixels, but that the actual pixel values

have some variation around these average values. However, this may be

computationally expensive, and image histograms may not have clearly defined valley

points, often making the selection of an accurate threshold difficult.

One method that is relatively simple, does not require much specific knowledge of the

image, and is robust against image noise, is the following iterative method,


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1. An initial threshold (T) is chosen; this can be done randomly or according to

any other method desired.

2. The image is segmented into object and background pixels as described above,

creating two sets:

1. G1 = {f(m,n):f(m,n)>T} (object pixels)

2. G2 = {f(m,n):f(m,n)< T} (background pixels) (note, f(m,n) is the value

of the pixel located in the mth column, n th row)

3. The average of each set is computed.

1. m1 = average value of G1

2. m2 = average value of G2

4. A new threshold is created that is the average of m1 and m2

1. T = ( m1 + m2)/2

5. Go back to step two, now using the new threshold computed in step four, keep

repeating until the new threshold matches the one before it.

This iterative algorithm is a special one-dimensional case of the k-means clustering

algorithm, which has been proven to converge at a local minimum meaning that a

different initial threshold may give a different final result.

In this approach threshold selection is done by Adaptive Thresholding using fuzzy

methodology to the sorted row vector containing the data (Non-Zero) in increasing

order.

3.3.2 Fuzzy methodology for automatic thresholding

Fuzzy set theory assigns a membership degree to all elements among the universe of

discourse according to their potential to fit in some class. The membership degree can

be expressed by a mathematical function that assigns, to each element in the set, a


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membership degree between 0 and 1. Let be the universe (finite and not empty) of

discourse and an element of. A fuzzy set in is defined as

= ( , )

The -function is used for modeling the membership degrees [18].This type of function

is suitable to represent the set of bright pixels and is defined as

Where, b = (1/2) (a+c). The S-function can be controlled through parameters a and c.

Parameter b is called the crossover point where = 0.5 . Higher the gray level of

a pixel (closer to white), the higher membership value and vice versa. A typical shape

of the -function is presented in Fig. 1. The -function is used to represent the dark

pixels and is defined by an expression obtained from -function as follows:

= ; . . = 1 ( ; , , )

Both membership functions could be seen, simultaneously, in Figure 3.4.

Figure 3.3 Typical Shape of S-Function Figure 3.4. Histogram of S-Function


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In proposed methodology contain some steps which are as follows;

Firstly one function is get defined in which input data a sorted row vector is

containing the data in increasing order and we get the output in the form of

row vector having segmented data.

Assume S shaped Curve, consider its start value, peak value and cross over

value. On the basis of these three values with the sorted data we have to

calculate the S-Membership function for the particular sorted data.

After finding membership function calculates the membership value for each

element in the data matrix.

Calculating the threshold using the help of membership function.

Finally after finding threshold the data matrix get binaries it means foreground

and background are get separated.

This fuzzy method for the selection of threshold is very useful in color image

segmentation. By using this terminology the inverted image also get segmented very

cleanly. The main advantage of this method is that there is no hard code threshold

selection is required for the binarization. The algorithm itself is capable of calculating

the threshold automatically for any image. But it will more effective on the images

with the uniform background.

3.4 FOREGROUND AND BACKGROUND SEPARATION

Reading of the foreground text is difficult in documents having multi colored complex

background. Automatic foreground text separation in such document images is very

much essential for smooth reading of the document contents. Separation of text

information from complex background in color document images remains a

challenging problem in character recognition applications such as bank cheque


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processing, postal address sorting. In day today life we come across many documents

that are designed deliberately with colorful and complex backgrounds such as travel

tickets, grade sheets and decorative postal envelopes. Presence of uniform/non-

uniform background patterns, multiple colors of the background, mix up of

foreground text color with background color in such documents cause difficulties in

reading the document contents. Also automatic O-ring of such documents results in

low character recognition accuracy.

The simplest method for extraction of text from the background is thresholding.

Global thresholding techniques extract objects from images having uniform

background. Local thresholding methods are suggested to withstand the adverse effect

of varying background but at a price of processing cost. The processing cost can be

reduced by capturing the regions containing text and then thresholding only those

regions instead of thresholding the entire document image.

Some binarization or foreground extraction techniques that have been proposed for

color document images are broadly based on color clustering or color segmentation

principle and in this sense, they are quite different from traditional threshold selection

algorithms. However, despite the large number of proposed algorithms for color

image segmentation only a handful of them have found direct application for the

document image processing. This is so because use of classical segmentation

algorithm exhibit difficulties to tackle several document defects like stains, humidity

marks, degradation of ink, paper, etc. Large size of color document images is another

bottleneck for efficient use of the traditional segmentation strategies. Rather, generic

algorithms, in few cases, have been customized in several ways for efficient

background-foreground separation in color document images.


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3.4.1 Need of background and foreground separation

As we know the segmentation is the very first step in the image processing. So the

background and foreground separation is also the very much essential step in image

segmentation. It also known as the Binarization of image, in which each pixel gets set

in between 0 and 1. As segmentation has many applications in image processing, so

background and foreground separation is also very important for,

Video surveillance.

Traffic monitoring.

Human detection.

Video editing

The background and foreground separation is the very essential and important part of

image segmentation in image processing. The different methods are available for the

separation of background and foreground separation. Different people used different

algorithms and method for binarization or background and foreground separation.

In our approach as name suggested that by using adaptive or automatic thresholding

we have to find threshold and then by setting the particular threshold value we have

binaries the image. After detecting the thresholding value the foreground and the

background of image get separated.

3.4.2 Outliers detection

The outliers of an object are defined as elements of object which lie at the boundary in

some representation. In our case the outliers of an image are defined as pixels which

lie at boundary in true representation of image or some other representation of image.

Since in the case of foreground and background separation ( binary segmentation) in

case of static and uniform background, number of pixels belonging to background

will be much more than the number of pixels belonging to foreground, therefore the


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foreground will lie at the boundaries or at the extremes in normalized histogram

representation of an image.

Figure 3.5 shows a gray image and figure 3.6 shows possible background and

foreground in histogram representation of image. It can be clearly observed that the

number of pixels belonging to foreground is much lower than number of pixels

belonging to background.

Fig 3.5 Gray Image

Figure 3.6 Normalized histogram indicating background and foreground


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Figure 3.7 below shows the binary image after using the information of foreground

and background from normalized histogram in figure 3.6.

Figure 3.7 Binary image

The white pixels in figure 3.7 show the foreground and the black pixels shows the

background. It is clear from figure 3.7 that the foreground includes all the objects of

interest.

3.5 MORPHOLOGICAL EROSION AND DILATION

Morphology is a broad set of image processing operations that process images based

on shapes. Morphological operations apply a structuring element to an input image,

creating an output image of the same size. In a morphological operation, the value of

each pixel in the output image is based on a comparison of the corresponding pixel in

the input image with its neighbors. By choosing the size and shape of the

neighborhood, you can construct a morphological operation that is sensitive to

specific shapes in the input image.

Morphological operations are affecting the form, structure or shape of an object.

Applied on binary images (black & white images Images with only 2 colors: black

and white). They are used in pre or post processing (filtering, thinning, and pruning)

or for getting a representation or description of the shape of objects/regions


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(boundaries, skeletons convex hulls). The most basic morphological operations are

dilation and erosion. Dilation adds pixels to the boundaries of objects in an image,

while erosion removes pixels on object boundaries. The number of pixels added or

removed from the objects in an image depends on the size and shape of the structuring

element used to process the image. In the morphological dilation and erosion

operations, the state of any given pixel in the output image is determined by applying

a rule to the corresponding pixel and its neighbors in the input image. The rule used to

process the pixels defines the operation as dilation or erosion is as follows.

Table3.1 Rules for Dilation and Erosion

Operation Rules

Dilation The value of the output pixel is the maximum value of all thepixels in the input pixel's neighborhood. In a binary image, if anyof the pixels is set to the value 1, the output pixel is set to 1.

Erosion The value of the output pixel is the minimum value of all thepixels in the input pixel's neighborhood. In a binary image, if anyof the pixels is set to 0, the output pixel is set to 0.

3.5.1 Dilating an image

To dilate an image, use the imdilate function. The imdilate function accepts two

primary arguments:

The input image to be processed (grayscale, binary, or packed binary image)

A structuring element object, returned by the strel function, or a binary matrix

defining the neighborhood of a structuring element

imdilate also accepts two optional arguments: SHAPE and PACKOPT . The SHAPE

argument affects the size of the output image. The PACKOPT argument identifies the

input image as packed binary.


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Dilation of image f by structuring element s is given by f s. The structuring element

s is positioned with its origin at (x, y) and the new pixel value is determined using the

rule:

Following figure shows the Dilation process of an image. Also there are some

examples shown below.

Figure 3.8 Original image 1 Figure 3.9 Processed image1

Original image Dilation by 3*3 square Dilation by 5*5 squareStructuring element structuring element

Figure3.10 Texture dilation 1

otherwise0

hitsif 1),(

f s y xg


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Figure3.11 Shape dilation 1

3.5.2 Eroding an image

To erode an image, use the imerode function. The imerode function accepts two

primary arguments:

The input image to be processed (grayscale, binary, or packed binary image)

A structuring element object, returned by the strel function, or a binary matrix

defining the neighborhood of a structuring element

imerode also accepts three optional arguments: SHAPE, PACKOPT , and M .

The SHAPE argument affects the size of the output image. The PACKOPT argument

identifies the input image as packed binary. If the image is packed binary, M identifies

the number of rows in the original image. The following example erodes the binary

image;

Read the image into the MATLAB workspace.

o BW1 = imread ('circbw.tif');

Create a structuring element. The following code creates a diagonal

structuring element object.

Call the imerode function, passing the image BW and the structuring element

SE as arguments.

o BW2 = imerode (BW1, SE);


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Erosion of image f by structuring element s is given by f s. The structuring element

s is positioned with its origin at (x, y) and the new pixel value is determined using the

rule:

Following figure shows the Erosion process of an image. Also there are some

examples shown below.

Figure3.12 Original Image 2 Figure3.13 Processed Image 2

Figure3.14 Texture Erosion 2

otherwise0 fitsif 1),( f s y xg


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Figure3.15 Shape Erosion 2

Dilation and erosion are often used in combination to implement image processing

operations. For example, the definition of a morphological opening of an image is

erosion followed by dilation, using the same structuring element for both operations.

The related operation, morphological closing of an image, is the reverse: it consists of

dilation followed by erosion with the same structuring element.

3.5.3 Connected component analysis

In binary images analysis objects are usually extracted by means of the connected

components labeling operation, which consists in assigning a unique label to each

maximal connected region of foreground pixels. Connected-component labeling is

used in computer vision to detect connected regions in binary digital images, although

color images and data with higher-dimensionality can also be processed. When

integrated into an image recognition system or human-computer interaction interface,

connected component labeling can operate on a variety of information. Blob

extraction is generally performed on the resulting binary image from a thresholding

step. Connected components labeling scans an image and groups its pixels into

components based on pixel connectivity, i.e. all pixels in a connected component

share similar pixel intensity values and are in some way connected with each other.

Once all groups have been determined, each pixel is labeled with a gray level or a

color (color labeling) according to the component it was assigned to.
http://en.wikipedia.org/wiki/Computer_visionhttp://en.wikipedia.org/wiki/Regionhttp://en.wikipedia.org/wiki/Binary_imagehttp://en.wikipedia.org/wiki/Digital_imagehttp://en.wikipedia.org/wiki/Color_imagehttp://en.wikipedia.org/wiki/Image_recognitionhttp://en.wikipedia.org/wiki/Human-computer_interactionhttp://en.wikipedia.org/wiki/Binary_imagehttp://homepages.inf.ed.ac.uk/rbf/HIPR2/pixel.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/connect.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/value.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/value.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/connect.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/pixel.htmhttp://en.wikipedia.org/wiki/Binary_imagehttp://en.wikipedia.org/wiki/Human-computer_interactionhttp://en.wikipedia.org/wiki/Image_recognitionhttp://en.wikipedia.org/wiki/Color_imagehttp://en.wikipedia.org/wiki/Digital_imagehttp://en.wikipedia.org/wiki/Binary_imagehttp://en.wikipedia.org/wiki/Regionhttp://en.wikipedia.org/wiki/Computer_vision


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How it works

Connected component labeling works by scanning an image, pixel-by-pixel

(from top to bottom and left to right) in order to identify connected pixel regions, i.e.

regions of adjacent pixels which share the same set of intensity values V . (For a binary

image V={1} ; however, in a image V will take on a range of values, for example:

V={51, 52, 53, ..., 77, 78, 79, 80} .) Connected component labeling works on binary

or gray level images and different measures of connectivity are possible. However, for

the following we assume binary input images and 8-connectivity . The connected

components labeling operator scans the image by moving along a row until it comes

to a point p (where p denotes the pixel to be labeled at any stage in the scanning

process) for which V={1} . When this is true, it examines the four neighbors of p

which have already been encountered in the scan. Based on this information, the

labeling of p occurs as follows:

If all four neighbors are 0, assign a new label to p , else

if only one neighbor has V={1} , assign its label to p , else

if more than one of the neighbors have V={1} , assign one of the labels to p and

make a note of the equivalences.

After completing the scan, the equivalent label pairs are sorted into equivalence

classes and a unique label is assigned to each class. As a final step, a second scan is

made through the image, during which each label is replaced by the label assigned to

its equivalence classes. For display, the labels might be different gray level or colors.
http://homepages.inf.ed.ac.uk/rbf/HIPR2/binimage.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/gryimage.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/connect.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/connect.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/gryimage.htmhttp://homepages.inf.ed.ac.uk/rbf/HIPR2/binimage.htm


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3.6 SEGMENTATION

Segmentation is the very first step in almost all the image processing application

where the properties of objects in image need to be analyzed e.g. in medical imaging

problems like tumor detection, coronary vessels detection; in automotive vision in

vehicle detection; object recognition in content based image retrieval etc. The

objective of the image segmentation is to extract the dominant colors. The image

segmentation is very important to simplify an information extraction from images,

such as color, texture, shape, and structure. The applications of image segmentation

are diversely in many fields such as image compression, image retrieval, object

detection, image enhancement, and medical image processing. Several approaches

have been already introduced for image segmentation ,

The purpose of image segmentation is to partition an image into meaningful

regions with respect to a particular application

The segmentation is based on measurements taken from the image and might

be grey level, colour, texture, depth or motion

Usually image segmentation is an initial and vital step in a series of processes

aimed at overall image understanding

Applications of image segmentation include,

Identifying objects in a scene for object-based measurements such as size and

shape.

Identifying objects in a moving scene for object-based video compression

(MPEG4).


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Identifying objects which are at different distances from a sensor using depth

measurements from a laser range finder enabling path planning for mobile

robots.

Image segmentation is the foundation of object recognition and computer vision. In

general, image noise should be eliminated through image preprocessing. And there is

some specifically-given work (such as region extraction and image marking) to do

after the main operation of image segmentation. The final output of the proposed

algorithm is the segmented image.

3.7 CONCLUDING REMARK

The discussion in the previous chapter shows that, there are many problems comes

while segmenting the image. This chapter gives the proper methodology and an

innovatively new approach to solve the many problems about segmentation.

Threshold selection is the main difficulty in the segmentation. This approach provides

the best solution on this. The threshold value is automatically getting selected for

segmenting an image. Morphological operations used in this approach helps to

improve the final segmented output.

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Chapter 3 Methdology