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Segmentation of Images

Using Edge Detection. By : Al-baihan, Abdullah 1

Digital Image Processing Project

Segmentation of Images Using Edge Detection.

By : Al-baihan, Abdullah

Date : Tuesday 17/4/2007



I. Introduction:

The Image Processing is part of computer Sciences, It is applying in

many domain in the world like; medicine it can use in Breast Cancer

Detection, in space, in Speech recognition ,in Handwriting recognition ,in

Biometric identification, in Document classification ,In industry , Optical

Character Recognisation, Internet search engines , etc.

The Image Processing has some Fundamentals steps like;

1-Image Acquisition:

Image acquisition is the first and most important step in Digital

Imaging Systems. All deconvolution algorithms need high quality

images.[1]

2-Filtering:

Filtering is often used in image processing to smooth noise, and to

enhance or detect features within an image. Some of filter is linear and

nonlinear.

3-Feature extraction:

Feature extraction is the operation to extract various image features for

identifying or interpreting meaningful physical objects from images. It is

involves using algorithms to detect and isolate various desired portions of a

digitized image or video stream.[2]

4-segmentation:

Segmentation procedures partition an image into its constituent

parts or objects. in general , autonomous segmentation is one of the most

difficult tasks in digital image processing. A rugged segmentation procedure

brings the process long way toward successful solution of imaging problems

that require objects to be identified individually. [3]



The segmentation step has some approaches like;

1-Edge-based methods.

Edge-based methods center around contour detection

2-Region-based techniques.

A region-based method usually proceeds as follows: the image is

partitioned into connected regions by grouping neighboring pixels of

similar intensity levels.

3-Connectivity-preserving relaxation methods.

The main idea is to start with some initial boundary shape

represented in the form of spline curves, and iteratively modify it by

applying various shrink/expansion operations according to some energy

function.

4-Threshold techniques.

Threshold techniques, which make decisions based on local pixel

information, are effective when the intensity levels of the objects fall

squarely outside the range of levels in the background.

The Threshold techniques has many methods like; Edge detection , it

is a problem of fundamental importance in image analysis.

My project is segmentation of Image using Edge information.

In chapter 1 I will explain the segmentation concept and available

approaches for Segmentation of image. In chapter2 I will explain the two

classes of image. In chapter3 I will explain the three classes of image. In

chapter4 I will explain the four classes of image. In the last of this report I

present some conclusion and Future works.



Chapter#1 Segmentation

In this chapter I will explain the Segmentation concept and the

available approaches for Segmentation of image ,In last of this chapter I

will present conclusion.

Segmentation procedures partition an image into its constituent

parts or objects. in general , autonomous segmentation is one of the most

difficult tasks in digital image processing. A rugged segmentation procedure

brings the process long way toward successful solution of imaging problems

that require objects to be identified individually. on the other hand, weak

or erratic segmentation algorithm almost always guarantee eventual

failure. in general , the more accurate the segmentation, the more likely

recognition is to succeed. [3] page 27.

Segmentation is a very important problem in digital image

processing. It use to classification the objects from the background and

other objects. when the objects are classification the Segmentation is stop.

In section 1.1 I will explain briefly Edge-based methods. In section 1.2

I will explain briefly Region-based techniques. In section 1.3 I will explain

briefly Connectivity-preserving relaxation methods. In section 1.4 I will

explain briefly Threshold techniques. In last of this chapter I will present

conclusion.



1.1 Edge-based methods.

Edge-based methods center around contour detection: their weakness

in connecting together broken contour lines make them, too, prone to

failure in the presence of blurring. [4]

1.2 Region-based techniques.

A region-based method usually proceeds as follows: the image is

partitioned into connected regions by grouping neighboring pixels of similar

intensity levels. Adjacent regions are then merged under some criterion

involving perhaps homogeneity or sharpness of region boundaries.

Overstringent criteria create fragmentation; lenient ones overlook blurred

boundaries and overmerge. Hybrid techniques using a mix of the methods

above are also popular. [4]

1.3 Connectivity-preserving relaxation methods.

A connectivity-preserving relaxation-based segmentation method,

usually referred to as the active contour model, was proposed recently. The

main idea is to start with some initial boundary shape represented in the

form of spline curves, and iteratively modify it by applying various

shrink/expansion operations according to some energy function. Although

the energy-minimizing model is not new, coupling it with the maintenance

of an ``elastic'' contour model gives it an interesting new twist. As usual



with such methods, getting trapped into a local minimum is a risk against

which one must guard; this is no easy task. [4]

1.4 Threshold techniques.

Threshold techniques, which make decisions based on local pixel

information, are effective when the intensity levels of the objects fall

squarely outside the range of levels in the background. Because spatial

information is ignored, however, blurred region boundaries can create

havoc. [4]

The Threshold techniques has many methods like; Edge information.

1.4.1 Edge detection.

Is a problem of fundamental importance in image analysis. In typical

images, edges characterize object boundaries and are therefore useful for

segmentation, registration, and identification of objects in a scene.

In general, the type of image is different according to number of

objects in the image. Like the following condition:

1. Two classes: Background and one object.

Before Segmentation After Segmentation



2. Three classes: Background and two different object classes.

3.

4.


3. Four classes : Background and three different classes.


In my project I will use the threshold techniques to estimate thresholds in

image for four classes Images.



conclusion

In this chapter I explained the Segmentation concept and the

available approaches for Segmentation of image , Segmentation is a very

important problem in digital image processing. It use to classification the

objects from the background and other objects. when the objects are

classification the Segmentation is stop.

briefly Edge-based methods, Region-based techniques , Connectivity-

preserving relaxation methods and Threshold techniques. The Threshold

techniques has many methods like; Edge information. In general, the type

of image is different according to number of objects in the image. Two

classes: Background and one object. Three classes: Background and two

different object classes. Four classes: Background and three different

classes.



Chapter#2 Segmentation Algorithm for two Classes Images:

In this Chapter, I will discuss the proposed algorithm for two classes

image segmentation. By two classes I mean images that have only

background and single class of objects. The diagram shown below depicts in

details the algorithm and how the threshold is selected.

In This chapter I will show the result of applying he proposed

segmentation algorithm using edge information. I will present the square

shape result without noise and with 3.0, 5.0, 7.0 noise. And the histogram for

each result. And I will present the Circle shape result without noise and with

3.0, 5.0, 7.0 noise. And the histogram for each result. I will present the real

image "flower" result without noise and with 3.0, 5.0, 7.0 noise. And the

histogram for each result.

Initially, the algorithm takes the input image (I) to be segmented and

edge detected image (K) and scan the edge detected image (K) to find edges.

After that it applies the segmentation process on the input image as shown

below to get the threshold value. Then I do simple thresholding on the input

image.



Input Image (I)

(I)

Edge Detection

Edge Detected

image

(K)

For each white pixel in image (K) { Take 4 windows of size n, horizontal &

vertical & Diagonal. For each window, compute their sums (s1 & s2).

D0 = |s1 – s2| D90 = |s3 – s4| D45 = |s5 – s6| D135 = |s7 – s8| If(Max (D0, D90, D45, D135) = D0

V[c++] = (s1+s2)/ (n*n*2) Else Else If(Max (D0, D90, D45, D135) = D90 V[c++]= (s3+s4)/(n*n*2) If(Max (D0, D90, D45, D135) = D45

V[c++]= (s5+s6)/(n*n*2) Else V[c++]= (s7+s8)/(n*n*2) H=Histogram(V, c); After scanning, Select Max in h to be the

threshold }

s1

s2 s3 s4

Segmented image

S5

S6 S7

S8



The first result: Square shape.

Ideal

Original

Histogram

Output



Noise7.0

Original

Histogram

Output



The second result: Circle shape.

Noise 7.0

Original

Histogram

Output



The third result: real image “Flower”.

Noise 7.0

Original

Histogram

Output



Conclusion:

I found the result of circle with 7.0 noise is better than result of

square with 7.0 noise and result of flower with 7.0 noise. Also I found

result of square with 7.0 noise is better than result of flower with 7.0 noise.



Chapter#3 Segmentation Algorithm for three Classes Images:

In this chapter , I will discuss the proposed algorithm for three classes

image segmentation. By three classes I mean images that have background

and two class of objects. The diagram shown below depicts in details the

algorithm and how the threshold is selected.


segmentation algorithm using edge information. I will present the square and

Circle shape result without noise and with 3.0, 5.0, 7.0 noise. And the

histogram for each result. I will present the real image "flower" result

without noise and with 3.0, 5.0, 7.0 noise. And the histogram for each result.




below to get the histogram. Then it apply ISO-DATA on the histogram to

compute the T1 and T2. Then I do thresholding on the input image based on

the computed T1 & T2.



Input Image (I)

(I)

Edge Detection

Edge Detected

image

(K)

For each white pixel in image (K) {

Take 4 windows of size n, horizontal & vertical & Diagonal. For each window, compute their sums (s1 & s2).

D0 = |s1 – s2| D90 = |s3 – s4| D45 = |s5 – s6| D135 = |s7 – s8| If(Max (D0, D90, D45, D135) = D0 V[c++] = (s1+s2)/ (n*n*2) Else If(Max (D0, D90, D45, D135) = D90 V[c++]= (s3+s4)/(n*n*2) If(Max (D0, D90, D45, D135) = D45 V[c++]= (s5+s6)/(n*n*2) Else V[c++]= (s7+s8)/(n*n*2) H=Histogram(V, c); T1,T2=ISO-Data(H); Threshold based on T1 & T2 }

s1

s2 s3 s4

S5

S6 S7

S8

Segmented image



The first result: Square & Circle shape.

Noise 7.0

Original

Histogram

Output



The second result: real image “Flower”.

Noise 7.0

Original

Histogram

Output



Conclusion

I note the result of image is enhancement after using segmentation

with three classes in comparison with two classes, this appear in flower

image.



Chapter#4 Segmentation Algorithm for four Classes Images

In this chapter , I will discuss the proposed algorithm for four classes

image segmentation. By four classes I mean images that have background

and three class of objects. The diagram shown below depicts in details the

algorithm and how the threshold is selected.


segmentation algorithm using edge information. I will present the two

squares and Circle in one shape, result without noise and with 3.0, 5.0, 7.0

noise. And the histogram for each result. I will present the real image

"Flower" result without noise and with 3.0, 5.0, 7.0 noise. And the histogram

for each result. I will present the real image "Photo" result without noise

and with 3.0, 5.0, 7.0 noise. And the histogram for each result. I will present

the real image "Hill" result without noise and with 3.0, 5.0, 7.0 noise. And

the histogram for each result. I will present the real image "Cell" result

without noise and with 3.0, 5.0, 7.0 noise. And the histogram for each result.

I will present the real image "Skin" result without noise and with 3.0, 5.0,

7.0 noise. And the histogram for each result. I will present the real image

"Beach" result without noise and with 3.0, 5.0, 7.0 noise. And the histogram

for each result.




below to get the histogram. Then it apply ISO-DATA-3 on the histogram to

compute the T1 , T2 and T3. Then I do thresholding on the input image

based on the computed T1 & T2&T3.



Input Image (I)

(I)

Edge Detection

Edge Detected

image

(K)

For each white pixel in image (K) {

Take 4 windows of size n, horizontal &

vertical & Diagonal. For each window, compute their sums (s1 & s2).

D0 = |s1 – s2| D90 = |s3 – s4| D45 = |s5 – s6| D135 = |s7 – s8| If(Max (D0, D90, D45, D135) = D0 V[c++] = (s1+s2)/ (n*n*2) Else If(Max (D0, D90, D45, D135) = D90

V[c++]= (s3+s4)/(n*n*2) If(Max (D0, D90, D45, D135) = D45 V[c++]= (s5+s6)/(n*n*2) Else V[c++]= (s7+s8)/(n*n*2) H=Histogram(V, c); T1,T2,T3=ISO-Data-3(H); Threshold based on T1 & T2 & T3 }

s1

s2 s3 s4

S5

S6 S7

S8

Segmented image



The first result: Two Squares & One Circle in one shape.

Noise 7.0

Original

Histogram

Output



The second result: Real Image “flower”.

Noise 7.0

Original

Histogram

Output



The third result: Real Image “ Hill ”.

Noise 7.0

Original

Histogram

Output



The fourth result: Real Image “ Cell ”.

Noise 7.0

Original

Histogram

Output

The fifth result: Real Image “ Beach ”.



Noise 7.0

Original

Histogram

Output

The sixth result: Real Image “ Skin ”.



Noise 7.0

Original

Histogram

Output

The seventh result: Real Image “ Photo ”.



Noise 7.0

Original

Histogram

Output

segmentasi gambar pada netbeans

Documents

classes of image

image acquisition

digitized image

image analysis

edge detection

segmentation step

segmentation concept

segmentation procedures