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Page 1: 2014 IEEE DOTNET IMAGE PROCESSING PROJECT A new iterative triclass thresholding technique in image segmentation

A NEW ITERATIVE TRICLASS THRESHOLDING

TECHNIQUE IN IMAGE SEGMENTATION

ABSTRACT

Image processing, segmentation is often the first step to pre-process images to extract

objects of interest for further analysis. Segmentation techniques can be generally categorized into

two frameworks, edge-based and region-based approaches. As a segmentation technique, Otsu’s

method is widely used in pattern recognition, document binarization, and computer vision.

Otsu’s method is used as a pre-processing technique to segment an image for further processing

such as feature analysis and quantification. Otsu’s method searches for a threshold that

minimizes the intra-class variances of the segmented image and can achieve good results when

the histogram of the original image has two distinct peaks, one belongs to the background, and

the other belongs to the foreground or the signal. The Otsu’s threshold is found by searching

across the whole range of the pixel values of the image until the intra-class variances reach their

minimum. In this paper, we present a new iterative method that is based on Otsu’s method but

differs from the standard application of the method in an important way. At the first iteration, we

apply Otsu’s method on an image to obtain the Otsu’s threshold and the means of two classes

separated by the threshold as the standard application does.

EXISTING SYSTEM:

GLOBALSOFT TECHNOLOGIESIEEE PROJECTS & SOFTWARE DEVELOPMENTS

IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE

BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS

CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401

Visit: www.finalyearprojects.org Mail to:[email protected]

GLOBALSOFT TECHNOLOGIESIEEE PROJECTS & SOFTWARE DEVELOPMENTS

IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE

BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS

CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401

Visit: www.finalyearprojects.org Mail to:[email protected]

Page 2: 2014 IEEE DOTNET IMAGE PROCESSING PROJECT A new iterative triclass thresholding technique in image segmentation

Otsu’s method, the new method starts to create better results as we can compare. And at

the final iteration, the new method creates much better results as shown., indicating that in the

process of searching for optimal TBD regions for segmentation, the new method is not adversely

affected by some low distance ratios at initial iterations. We showed the histogram of the original

and the changes of the thresholds. The thresholds monotonically decrease from a large value of

121.0 on iteration one, which is also the Otsu’s threshold, to a small value of 52.8 on iteration

seven, indicating that the iterative method progressively searches for weak objects to segment.

PROPOSED SYSTEM:

The original image and its Otsu’s result are shown respectively. We observe that the

standard Otsu’s method misses the weak nucleus at the bottom of the image. We then applied the

iterative method and show the result of the first and fourth iterations, which is the last iteration.

Experiments on another microscopic image with multiple nuclei. (a) A zoomed-in portion of

muscular nuclei acquired by fluorescence microscopy, the weak nuclei are marked by red arrow.

(b) The result of Otsu’s method. Note it missed the weak nuclied pointed by the arrows in (a).(c)

Results after the first iteration of the new method. (d) Results after the fourth iteration of the new

method. The nuclei missed by the standard Otsu’s method are now detected by the proposed

method. We note that although the final result does not fully segment the whole areas of the

weak nuclei, starting with the result we can recover the nuclei by using techniques such as

region-regrowing in post-processing. respectively. While the first iteration also misses the weak

nucleus, the algorithm detects the nucleus at the final iteration. As the last example, another

microscopic image consisting of many nuclei with various gray intensities was tested. The

original image contains some weakly stained nuclei, as pointed by the arrows. Result given by

Otsu’s method shows that the method misses weak nuclei. By applying the proposed method on

the same image, we found that the weak nuclei are gradually detected by the iterative process. At

the final result, all three weak nuclei are marked correctly. Here we note that though the final

segmentation result given by the new method do not reveal the full sizes of the three weak

nuclei, they can be readily recovered in post processing by techniques like region growing. In

Page 3: 2014 IEEE DOTNET IMAGE PROCESSING PROJECT A new iterative triclass thresholding technique in image segmentation

both examples, the iteration stops when the change in threshold T [i] is less than two. We also

computed the distance ratios and show the plots respectively. From the figure we observe that

despite very low distance ratios at the first iterations for both figures, the ratios quickly increase

from iteration two, suggesting that the TBD regions at each iteration provide favorable inputs for

Otsu’s method to process. The above examples on both synthetic and real images demonstrate

that the new method achieves superior performance in segmenting single or multiple objects,

even in very challenging cases.

IMPLEMENTATION

Implementation is the stage of the project when the theoretical design is turned out into a

working system. Thus it can be considered to be the most critical stage in achieving a successful

new system and in giving the user, confidence that the new system will work and be effective.

The implementation stage involves careful planning, investigation of the existing system

and it’s constraints on implementation, designing of methods to achieve changeover and

evaluation of changeover methods.

MODULES

A. Otsu’s threshold Method

B. Iterative Method

MODULES DESCRIPTION :

A. Otsu’s threshold Method

Otsu’s method searches the histogram of an image to find a threshold that binarizes the

image into two classes, the background with a mean of μ0 and the foreground with a mean of μ1,

as shown in the top .Without loss of generality, here we assume that the foreground is brighter

than the background. The calculation of threshold T is as follows. we can see that T is function of

Page 4: 2014 IEEE DOTNET IMAGE PROCESSING PROJECT A new iterative triclass thresholding technique in image segmentation

the pixel values of both the foreground and the background. If the signal intensity changes, it

may affect T in such a way that the segmentation result may become less optimal. Next we use

an example to illustrate the effect of signal intensity on the calculation of T. Fig. 2(a) shows an

original image consisting of multiple objects in gray scale. The segmentation result of the

standard Otsu’s method is shown in Fig. 2(b), from which we can observe that most objects are

correctly segmented or marked. Then we purposely added a strong object to the original image to

increase the overall signal intensity in the foreground and tested how Otsu’s method performs in

this case. The new test image is shown in Fig. 2(c) and the corresponding Otsu’s result is shown

in Fig. 2(d). Though one would expect that with added signal intensity the segmentation result

should be equally good or better, the result of Fig. 2(d) shows that some weak objects are

actually missed now by Otsu’s method. As shown above, there are cases that Otsu’s method does

not produce satisfactory results even when the foreground has a high signal intensity, i.e., a

higher signal-to-background ratio (SBR). In other words, the performance of Otsu’s method is

not a function of SBR only.

B. Iterative Method

The idea of dividing an image’s histogram iteratively into three classes is illustrated at the

bottom of Fig. 1. For an image u, at the first iteration, Otsu’s method is applied to find a

threshold T [1] where the superscript denotes the number of iteration. We then find and denote

the means of the two classes separated by T [1] as μ[1] 0 and μ[1] 1 for the background and

foreground, respectively. Then we classify regions whose pixel values are greater than μ[1] 1 as

foreground F[1] and regions whose pixel values are less than μ[1]0 as background B[1]. For the

remaining pixels u(x, y) such that μ[1]0≤ u(x, y) ≤ μ[1]1 we denote them as the TBD class _[1].

So our iterative process assumes that the pixels that are greater than the mean of the “tentatively”

determined foreground are the true foreground. Similarly, pixels with values less than μ0 are for

certain the background. But the pixels in the TBD class, which are the ones that typically cause

misclassifications in the standard Otsu’s method, are not decided at once and will be further

processed.

CONSLUSION

Page 5: 2014 IEEE DOTNET IMAGE PROCESSING PROJECT A new iterative triclass thresholding technique in image segmentation

As Otsu’s method is widely used as a pre-processing step to segment images for further

processing, it is important to achieve a high accuracy. However, since Otsu’s threshold is biased

towards the class with a large variance, it tends to miss weak objects or fine details in images.

For example in biomedical images, nuclei and axons may be imaged with very different

intensities due to uneven staining or imperfect lightening conditions, raising difficulty for

algorithms like Otsu’s method to successfully segment them. Without a robust segmentation

results, more sophisticated processing such as tracking and feature analysis become highly

challenging. In this paper, we proposed to take advantage of Otsu’s threshold by classifying

images into three tentative classes instead of two permanent classes in an iterative manner. The

three classes are designated as the true foreground and background, and a third TBD region that

is to be further processed at the next iteration. At each iteration, the tri-class approach keeps

regions that are determined to be foreground and background unchanged and focuses on the third

TBD region. At each succeeding iteration, the area of the TBD region decreases and more pixels

are assigned to the foreground and background classes. The iteration stops until the change in

thresholds of two consecutive iterations is less than a threshold. To assist on evaluating the

performance of the new algorithm we introduced the notion of distance ratio which measures a

posteriori how favorable an image or region is for Otsu’s method to segment. The performance

of the new algorithm is evaluated on both synthetic and real microscopic images. By assigning

very strong and very weak pixels to the tentative foreground and background classes, the new

method is less biased toward the class with a large variance than Otsu’s method does.

Experimental results demonstrate that the proposed algorithm can achieve superior performance

in segmenting weak objects and fine details. The new method is also almost parameter-free

except for the preset threshold to terminate the iterative process. The added computational cost is

minimal as the process usually stops in a few iterations and each iteration only processes a

monotonically shrinking TBD region. From a statistical analysis perspective, Otsu’s method is

optimal to separate a bi-modal histogram into two classes where the probability distribution

functions (PDFs) of the two classes have an approximately “tall and thin” shape. However, when

one or both PDFs have a “wide and flat” shape a single threshold determined by Otsu’s or other

method may not be sufficient to correctly separate the two classes as some pixels in the signal

class may appear closer to the noise class on the histogram and become difficult to segment.

Indeed, in Otsu’s method, as well as in many segmentation methods, a common challenge is to

Page 6: 2014 IEEE DOTNET IMAGE PROCESSING PROJECT A new iterative triclass thresholding technique in image segmentation

decide which pixels should or should not be binarized by a common threshold. If we have this

information, then we can design perfect segmentation algorithm for virtually no errors. Testing

results show that the new method can achieve better performance in challenging cases. We note

that there are many segmentation methods, but many of them require careful selection of

parameters to achieve satisfactory performance. From this perspective, a parameter-free method

may be well suited in many applications.

SYSTEM SPECIFICATION

Hardware Requirements:

• System : Pentium IV 2.4 GHz.

• Hard Disk : 40 GB.

• Floppy Drive : 1.44 Mb.

• Monitor : 14’ Colour Monitor.

• Mouse : Optical Mouse.

• Ram : 512 Mb.

Software Requirements:

• Operating system : Windows-7 32 bit Ultimate OS.

• Coding Language : C#.Net

• Front-End : Visual Studio 2010 Ultimate.

• Data Base : SQL Server 2008.

Page 7: 2014 IEEE DOTNET IMAGE PROCESSING PROJECT A new iterative triclass thresholding technique in image segmentation

decide which pixels should or should not be binarized by a common threshold. If we have this

information, then we can design perfect segmentation algorithm for virtually no errors. Testing

results show that the new method can achieve better performance in challenging cases. We note

that there are many segmentation methods, but many of them require careful selection of

parameters to achieve satisfactory performance. From this perspective, a parameter-free method

may be well suited in many applications.

SYSTEM SPECIFICATION

Hardware Requirements:

• System : Pentium IV 2.4 GHz.

• Hard Disk : 40 GB.

• Floppy Drive : 1.44 Mb.

• Monitor : 14’ Colour Monitor.

• Mouse : Optical Mouse.

• Ram : 512 Mb.

Software Requirements:

• Operating system : Windows-7 32 bit Ultimate OS.

• Coding Language : C#.Net

• Front-End : Visual Studio 2010 Ultimate.

• Data Base : SQL Server 2008.