Comparative performance analysis of segmentation techniques

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –

6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME

238

COMPARATIVE PERFORMANCE ANALYSIS OF SEGMENTATION

TECHNIQUES

Amandeep Singh

ECE, Lovely professional university, near

Phagwara Punjab, India

[email protected]

A.P Gursimran singh sandhu

ECE, Punjab Technical University

[email protected]

ABSTRACT

The study presented in this article focuses on comparative analysis of Segmentation techniques.

Segmentation techniques are applied to extract Region of Interest (ROI) from medical images obtained

from different medical scanners such as Ultrasound, CT or MRI. Global thresholding, Adaptive

Thresholding, Region grow and Active contour using level set techniques has been used in the proposed

segmentation analysis. The approach consists of two steps: Apply segmentation technique to extract most

discriminative regions from image and calculate the parameters from the resulting image obtained by the

applied techniques. Parameters are precision, accuracy sensitivity, specificity. Segmentation techniques

have been tested on medical and synthetic data sets and results are compared with each other. Tests

indicate that using level set contour significantly improves the ability of extracting region of interest with

unbroken boundaries and Adaptive thresholding acquires most of the details present in the image. Manual

global thresholding have the highest success rate of extracting the region of interest.

Keywords

Global threshold; Adaptive threshold; Region grow; Level set contour; Binary classification; Hybrid segmentation

I. INTRODUCTION

The research presented in this article is part of an on-going Mtech thesis aimed at developing an

automated hybrid imaging system for segmentation of tumor present in medical images obtained by

Computed Tomography (CT) scans. Farzaneh Keyvanfard et al [1] Segmenting of human organs in CT

scans using gray level information is particularly challenging due to the changing shape of organs in

medical images and the gray level intensity overlap in soft tissues. Medical image segmentation requires

extracting specific features from an image by distinguishing objects from the background. Medical image

segmentation aims to separate known anatomical structures from the background for research, cancer

diagnosis, quantification of tissue volumes, radiotherapy treatment planning and study of anatomical

structures.

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Cancer diagnose can be manually performed by a human expert who simply examines an image,

determines borders between regions, and classifies each region this process is called segmentation in

terms of image processing. This is perhaps the most reliable and accurate method of image segmentation

because the human visual system is immensely complex and well suited to the task. But the limitation

starts in volumetric images due to the quantity of clinical data. Implementation of image processing

increase the rate of similar CT interpretation between different analysers, now its just 20% and to relief

for the analyzers from routine CT analysis.Nader H. Abdel-massieh et al [2] [3], thresholding is

commonly used image segmentation technique, In this method, pixels that are alike in grayscale (or some

other feature) are grouped together. Often a image histogram is used to determine the best setting for the

threshold. After thresholding image is converted into logical image the pixels range above threshold

become 1 or white pixels and pixel range below threshold become 0 or black pixels. Bio medical images

may have multiple modes and multiple thresholds may be helpful. In general multilevel thresholding is

less reliable than single level thresholding. Mostly because it is very difficult to determine thresholds that

adequately separate objects of interest. N. Otsu et al [4] Global Thresholding choose threshold T that

separates object from background global thresholding is a single threshold method of thresholding

technique. When the pixel values of the components and that of background are fairly consistent in their

respective values over the entire image, global thresholding could be used. In adaptive thresholding,

different threshold values T1,T2,T3 etc for different local areas are used. This more sophisticated version

of thresholding can accommodate changing lighting conditions in the image. The fundamental drawback

of histogram-based region detection is that histograms provide no spatial information (only the

distribution of gray levels).

Region-growing approaches exploit the important fact that pixels which are close together have similar

gray values. The first region-growing method was the seeded region growing method. This method takes

a set of seeds as input along with the image. The seeds mark each of the objects to be segmented. The

regions are iteratively grown by comparing all unallocated neighboring pixels to the regions. The

difference between a pixel's intensity value and the region's mean is used as a measure of similarity. The

pixel with the smallest difference measured this way is allocated to the respective region. This process

continues until all pixels are allocated to a region. Seeded region growing requires seeds as additional

input. The segmentation results are dependent on the choice of seeds. Noise in the image can cause the

seeds to be poorly placed. Unseeded region growing is a modified algorithm that doesn't require explicit

seeds. Region Growing offers several advantages over conventional segmentation techniques. Unlike

gradient and Laplacian methods, the borders of regions found by region growing are perfectly thin (since

we only add pixels to the exterior of our Region) and connected. The algorithm is also very stable with

respect to noise. Region will never contain too much of the background, so long as the parameters are

defined correctly. Other techniques that produce connected edges, like boundary tracking, are very

unstable. Most importantly, membership in a region can be based on multiple criteria. We can take

advantage of several image properties, such as low gradient or gray level intensity value, at once.

An important class of segmentation methods is model based methods. Caselles, R. Kimmel [5] [6] Active

Contours, also known as Evolving Fronts . Active contour is an interface usually used to separate

structures and background on the image. There are two principal approaches to build an active contour:

explicit or Lagrangian approach, and resulting interfaces called snakes, implicit or Eulerian approach, and

resulting interfaces called level sets. These methods are used in the domain of image processing to locate

the contour of an object. Trying to locate an object contour purely by running a low level image

processing task such as canny edge detection is not particularly successful. Often the edge is not

continues, i.e. there might be holes along the edge, and spurious edges can be present because of noise.

The level set method makes it very easy to follow shapes that change topology, for example when a shape

splits in two, develops holes, or the reverse of these operations. An active contour tries to improve on this

by imposing desirable properties such as continuity and smoothness to the contour of the object. This



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means that the active contour approach adds a certain degree of prior knowledge for dealing with the

problem of finding the object contour. An active contour is modeled as parametric curve, this curve aims

to minimize its internal energy by moving into a local minimum. In this paper we use level set contour

method along with other methods for analysis.

This paper introduces the use wide range of data set and presents a comprehensive analysis determining

the optimal segmentation technique for the applied to CT scans. This paper is focusing on a robust

implementation technique for segmenting medical volumes and performing binary analysis methodology

on images obtained from a CT scanner. The rest of this paper is organized as follow: The following

section illustrates the proposed medical image segmentation system analysis methodology have been

explained in section 2. The results and analysis of the implemented of segmentation techniques for

medical image segmentation is illustrated in section 3. Finally, section 4 includes the conclusions and

future scope.

II. METHODOLOGY

Binary classification problems often result in a predicted probability surface, which is then translated into

a presence–absence classification map in more generalize way it is act of discriminating an item into one

of two groups based on specified measures or variables. This paper consists of four main binary

classification methods sensitivity, specificity, accuracy and precision. Sensitivity and specificity are

statistical measures of the performance of a binary classification test, also known in statistics as

classification function. Sensitivity (also called recall rate in some fields) measures the proportion of actual

positives which are correctly identified as such (e.g. the percentage of sick people who are correctly

identified as having the condition). Specificity measures the proportion of negatives which are correctly

identified (e.g. the percentage of healthy people who are correctly identified as not having the condition).

The accuracy of a measurement system is the degree of closeness of measurements of a quantity to that

quantity's actual(true) value.

Figure 1. Process Diagram

The precision of a measurement system, also called reproducibility or repeatability, is the degree to which

repeated measurements under unchanged conditions show the same results. Image is first converted in to

gray scale because resulting images of biomedical scans are gray in nature .Salt and paper noise is used in

noise addition process, different segmentation techniques are applied on the resulting image of noise

addition process. Every applied segmentation technique produces a segmented image which is in logical

scale. Binary classification methodology is used to statistically represent the performance of each segment

IMAGE NOISE ADDITION SEGMENTATION

TECHNIQUES

BINARY

CLASSIFICATION

COMPARISION

CHART



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technique and comparison is done on basis values obtained by binary classification methods. These

methods of classification simply work on existence of desired and undesired objects, edges in the image.

Binary classifier use terms to indicate presence of a edges and objects. Some of the images have the

objects, clear edges and our test says they are positive. They are called true positives (TP). Some have the

objects, but the test claims they don't. They are called false negatives (FN). Some don't have the objects,

and the test says they don't - true negatives (TN). Finally, we might have image with no clear edges that

have a positive test result - false positives (FP). Thus, the number of true positives, false negatives, true

negatives, and false positives add up to 100% of the set. Specificity (TNR) is the proportion of objects

that tested negative (TN) of all the objects that actually are negative (TN+FP). As with sensitivity, it can

be looked at as the probability that the test result is negative given that the image is not having object.

With higher specificity, fewer will be the undesired objects which r not present in original image.

Sensitivity (TPR) is the proportion of objects that tested positive (TP) of all the objects that actually are

positive (TP+FN). It can be seen as the probability that the test is positive given that the presence of

object in image.

The accuracy is the proportion of true results (both true positives and true negatives) in the population. Precision or

positive predictive value is defined as the proportion of the true positives against all the positive results (both true

positives and false positives). An accuracy of 100% means that the measured values are exactly the same as the

given values. In the medical domain, the most important performance measures are both specificity and sensitivity.

Optimally one would want both high specificity and high sensitivity measures. However, theoretically these two

measures should have a negative correlation. Since accuracy reflects both the sensitivity and specificity in relation to

each other, this descriptor was selected to determine the overall correctness of the classifier. To evaluate the

performance of each classifier; specificity, sensitivity, precision, accuracy rates are then calculated from each of the

misclassification matrices.

Table1-Description table of TP, TN, FP, FN

Table 2-Defination of binary classification

True positive (TP) Correctly identified

True negative (TN) Correctly rejected

False positive (FP) Incorrectly identified

False negative(FN) Incorrectly rejected

Sensitivity True Positive / Total Positive

Specificity True Negative / Total Negatives

Accuracy (True Positives + True Negatives) / (True

Positive + True Negative + False Positive +

False Negative)

Precision True Positive / (True Positive + False

Positives)



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III. RESULTS The segmentation algorithms are applied to various formats of images and results are given below. The binary

analysis Parameters has value range from 0 to 1.

(a) (b) (c) (d) (e) (f) Figure 2. Segmentation results for Eye image .(a) Original eye image effected by salt paper noise (b) Global threshold applied on original eye image (c) Result

of Adaptive threshold segmentation applied on eye image.(d) Eye image result of region grow thin segmentation (e) Eye image result of region grow thin

segmentation (c) Result of Level set segmentation applied on eye image.

(a) (b) (c) (d) (e) (f)

Figure 3. Segmentation results for Bone image .(a) Original bone image effected by salt paper noise (b) Global threshold applied on original bone image (c)

Result of Adaptive threshold segmentation applied on bone image.(d) Bone image result of region grow thin segmentation (e) Bone image result of region

grow thin segmentation (c) Result of Level set segmentation applied on Bone image.

(a) (b) (c) (d) (e) (f)

Figure. 4. Segmentation results for Lena image .(a) Original lena image effected by salt paper noise (b) Global threshold applied on original lena image (c)

Result of Adaptive threshold segmentation applied on lena image.(d) Lena image result of region grow thin segmentation (e) Lena image result of region

grow thin segmentation (f) Result of Level set segmentation applied on lena image.

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print),

ISSN 0976 – 6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME

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(a) (b) (c) (d) (e) (f) Figure 5. Segmentation results for Hex shapes image .(a) Original hex shapes image effected by salt paper noise (b) Global threshold applied on original hex

shapes image (c) Result of Adaptive threshold segmentation applied on hex shapes image.(d) Hex shapes image result of region grow thin segmentation (e)

Hex shapes image result of region grow thin segmentation (f) Result of Level set segmentation applied on hex shapes image.

(a) (b) (c) (d) (e) (f)

Figure 6. Segmentation results for Frog image .(a) Original frog image effected by salt paper noise (b) Global threshold applied on original frog image (c)

Result of Adaptive threshold segmentation applied on frog image.(d) Frog image result of region grow thin segmentation (e) Frog image result of region

grow thin segmentation (f) Result of Level set segmentation applied on frog image.

(a) (b) (c) (d) (e) (f)

Fig. 7. Segmentation results for Frog image.(a) Original frog image effected by salt paper noise (b) Global threshold applied on original frog image (c) Result

of Adaptive threshold segmentation applied on frog image.(d) Frog image result of region grow thin segmentation (e) Frog image result of region grow thin

segmentation (f) Result of Level set segmentation applied on frog image.

Table 3. Results of Sensitivity applied on segmentation methods

Images Eye Bone Lena Frog Hex shapes Two cell

Techniques Sensitivity Sensitivity Sensitivity Sensitivity Sensitivity Sensitivity

Result of segmentation

after Global thresholding 0.75 1 1 1 0.25 1


after Adaptive thresholding 0.75 0.66 0.25 0.8 0.25 1

Result of segmentation after

region grow-thick 0.75 0 1 0.4 0.5 0


region grow-thin 0.75 0 1 0.4 0.5 0


level set 0 1 0 1 1 0


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Table 4. Results of Specificity applied on segmentation methods.

Images EYE Bone Lena Frog Hex Two cell

Techniques Specificity Specificity Specificity Specificity Specificity Specificity

Result of

segmentation

after Global thresholding

0.5 1 0.5 1 0.66 0.5


after Adaptive

thresholding

0.5 0 0.25 0.66 0 0.5


after region grow-thick

0.5 1 0.5 1 1 1


after region grow-thin 0.5 1 0.5 1 1 1


after level set 0.5 0.66 0 0.33 1 0

Table 5. Results of Accuracy applied on segmentation methods.

Images Eye Bone Lena Frog Hex shapes Two cell

Techniques Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy

Result of

segmentation


0.66 1 0.75 1 0.42 0.75


after Adaptive

thresholding

0.66 0.33 0.25 0.75 0.14 0.25


after region grow-thick 0.66 0.5 0.75 0.62 0.71 0.5


after region grow-thin 0.66 0.5 0.75 0.62 0.71 0.5


after level set 0.16 0.83 0 0.75 1 0

Table 6. Results of Precision applied on segmentation methods.

Images Eye Bone Lena Frog Hex Two cell

Techniques Precision Precision Precision Precision Precision Precision

Result of

segmentation


0.75 1 0.66 1 0.25 0.66


after Adaptive

thresholding

0.75 0.4 0.25 0.8 0.25 0.66


after region grow-thick 0.75 0 0.66 1 0.5 0


after region grow-thin 0.75 0 0.66 1 0.5 0


after level set 0 0.75 0 0.71 1 0

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976

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Table 7. Overall performance results of different segmentation methods.

Average Average

Techniques Sensitivity


after Global thresholding 0.83


after Adaptive thresholding 0.61

Result of segmentation afte

region grow-thick 0.44

Result of segmentation afte

region grow-thin 0.44


level set 0.5

Figure 8. Graphical representation of performance of segmentation methods.

IV. CONCLUSION

The image segmentation is a relevant technique in image processing. Numerous and varied methods exist

for many applications. Now that we have described the algorithms, we can compare the outputs and check

which type of segmentation technique is better for

classification is best analysis method for biomedical image key factors which allow for the use of a

segmentation algorithm in a Many object detection system:. Accuracy, Sensitivity, Precision, Specifici

On an average parameter set of the edge detection techniques, Global thresholding technique performed

better than all other techniques for all the formats of images, sample of which can be found in results.

Histogram based methods are found to be very

compared to other image segmentation methods. If significant peaks and valleys are identified properly

and proper thresholding is fixed, this technique yields good result. Region

Average

sensitivity

Average

Specificity

Results of segmentation after Global thresholding

Results of segmentation after region grow thick

Results of segmentation after Level set

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976

6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME

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Table 7. Overall performance results of different segmentation methods.

Average Average Average Average

Sensitivity Specificity Accuracy Precision

0.83 0.69 0.76 0.72

0.61 0.31 0.39 0.51

0.44 0.83 0.62 0.48

0.44 0.83 0.62 0.48

0.5 0.41 0.45 0.41

Figure 8. Graphical representation of performance of segmentation methods.



which type of segmentation technique is better for a particular format. It is believed that the binary


segmentation algorithm in a Many object detection system:. Accuracy, Sensitivity, Precision, Specifici



Histogram based methods are found to be very efficient in terms of computation complexity when


and proper thresholding is fixed, this technique yields good result. Region-grow technique operates wel

Average Accuracy Average Precision Overall

Performance

Results of segmentation after Global thresholding Results of segmentation after Adaptive thresholding

Results of segmentation after region grow thick Results of segmentation after region grow thin

Results of segmentation after Level set


Average Overall

Precision Performance

0.72 0.75

0.51 0.455

0.48 0.5925

0.48 0.5925

0.41 0.4425



a particular format. It is believed that the binary


segmentation algorithm in a Many object detection system:. Accuracy, Sensitivity, Precision, Specificity.



efficient in terms of computation complexity when


grow technique operates well

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Results of segmentation after Adaptive thresholding

Results of segmentation after region grow thin



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over all formats of images provided proper seed point is selected and range of threshold is properly

defined. This method performs well even when noise is present and it is reflected with a reasonable values

of parameters in table. Adaptive thresholding yield fine results over all but in some type of image it yield

better results than all other segmentation methods, we can observe it by resulting image of segmentation

methods . The level set algorithm is guaranteed to converge but it may not return optimal solution for

details of images. Region grow can enhance salt noise if seeds are not selected properly. As the adaptive

thresholding perform nearly close to global thresholding and adaptive thresholding is an automatic

procedure of segmentation it is a better choice for hybrid segmentation. Region grows and Adaptive

thresholding will be used for creating a hybrid segmentation method to improve the Detection and

segmentation of liver cancer from CT scan.

ACKNOWLEDGMENT

THE AUTHOR IS THANKFUL TO ALL THE STAFF MEMBERS OF THE SCHOOL OF ECE, LOVELY PROFESSIONAL

UNIVERSITY FOR THEIR VALUABLE SUPPORT.

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Comparative performance analysis of segmentation techniques