A novel medical image segmentation and classification using combined feature set and decision tree classifier
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The paper presents a novel medical image segmentation and classification method utilizing a semi-decision algorithm and decision tree classifier, aimed at enhancing diagnosis accuracy. The proposed approach focuses on segmenting tumor areas from CT images, demonstrating a segmentation accuracy of 70% and a classification accuracy of 94%. The study highlights the importance of effective segmentation techniques in improving automated medical image diagnosis.
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 09 | September-2015, Available @ http://www.ijret.org 83
A NOVEL MEDICAL IMAGE SEGMENTATION AND
CLASSIFICATION USING COMBINED FEATURE SET AND DECISION
TREE CLASSIFIER
V. V. Satyanarayana Tallapragada1
, D. Manoj Reddy2
, P. Shashi Kiran3
, D. Venkat Reddy4
1
Associate Professor, Department of ECE, Matrusri Engineering College, Hyderabad, India.
satya.tvv@gmail.com
2
B.E. IV/IV Student, Department of ECE, MGIT, Hyderabad, India.
manoj8101994@gmail.com
3
B.E. IV/IV Student, Department of ECE, MGIT, Hyderabad, India.
shashikiran_1995@yahoo.in
4
Professor, Department of ECE, MGIT, Hyderabad, India.
dasari.reddy@gmail.com
Abstract
Diagnosis is the first step before giving a medicine to the patient. In the recent past such diagnosis is performed using medical
images where segmentation is the prime part in the medical image retrieval which improves the feature set that is collected from
the segmented image. In this paper, it is proposed to segment the medical image a semi decision algorithm that can segment only
the tumor part from the CT image. Further texture based techniques are used to extract the feature vector from the segmented
region of interest. Medical images under test are classified using decision tree classifier. Results show better performance in
terms of accuracy when compared to the conventional methods.
Key Words: Medical Images, Decision Tree Classifier, Segmentation, Semi-decision algorithm
--------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
With the rapid increase in the population there is a need for
development of fast diagnosis in the field of medicine. Thus
an efficient and more effective method is required for better
diagnosis of medical image information. Segmentation is the
prime part in the automatic diagnosis of a disease where a
system is given an input such that it segments the input
image and further diagnoses based on the intelligence that is
provided to it. Figure 1 shows the basic block diagram of a
medical diagnosis system.
Fig - 1. Basic Block Diagram of a medical diagnosis system.
The basic problem with such a system is the segmentation
process. Optimal algorithms need to be formulated for
proper segmentation of the region of interest such that
features can be extracted from the region of interest. Further
these features must be selected in such a way that they do
not pose any classification problems.
Content based image retrieval has gained much importance
in the recent past and this is applied over medical image
where based on the content of the medical image, similar
images are retrieved from the database. Several ways were
determined to improve the relevance of multi-modal
information retreival in medical system that are being
developed. There are several medical information retreival
systems. An attempt is made to get the metadata about the
image from the full text of the subject and retreive the
similar images in response to that of the query image is
provided[1]. Further a step forward in the diagnosis is done
which extracts the mycardial wall of the left and right
ventricles from cardiac CT images. The ventricles are
primarily located and then detected by first identifying the
endocardium and then segmenting it by locating it using
active contour model. This method proved to be robust and
accurate[2]. In order to analyse skin histopathologial images
for the diagnosis of skin cancer various computer aided
automated diagnosis techniques are developed. To improve
the speed fo the technique a FPGA based hybrid
implementation framework is developed[3]. Existing
segmentation techniques are used for segmenting only 2D](https://image.slidesharecdn.com/anovelmedicalimagesegmentationandclassificationusingcombinedfeaturesetanddecisiontreeclassifier-160916050834/85/A-novel-medical-image-segmentation-and-classification-using-combined-feature-set-and-decision-tree-classifier-1-320.jpg)
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 09 | September-2015, Available @ http://www.ijret.org 84
images. Hybrid Extreme Rotation Forest classifier is
developed for segmenting 3D CTA images which is a group
of classifier composing of extreme learning machine and
decision trees[4].Biomedical research has been extended by
segmenting the interested objects from the microscopic
images and classifying them. It is achieved using RBF
network combined with fuzzy and graph based discrete
approach[5]. With the rapid increase in the number of
images that are stored in the database there is a problem
with image annotation which cannot be manually done.
Hence it is the problem which is still pertinent and remained
despite the development of many content based multimedia
retrieval solutions[6]. The problem becomes more worse as
the images that are store may be homogeneous in nature.
Hence a technique was proposed which segments using 3D
points[7]. Further various algorithms exists which can
improve the contrast of the image as the images that are
acquired are of low quality in nature which need to be
enhanced for further processing[8].
With the technological advancement security lapse is of
major concern. Hence different techniques are adopted to
provide better security. In this juncture, biometrics is widely
used. Iris is one of such biometric which can provide high
security when compared to other existing biometric traits. A
novel segmentation method is developed for segmenting the
iris part which is occluded and can be seen partially[9].
Segmentation is primarily based on partitioning an image
based on the changes in the regions which is nothing but
based on the content of the image[10-11]. Edge in an image
which enmark the boundaries between two regions. Edge
based segmentation are used to detect the sharp
discontinuties in an image. Further such a edge based
segmentation can also be extended using various color
models[12-16].
This paper is organized in five sections. Section I introduces
the topic. Section. II clearly delineates the problem
definition and Section III elaborates the proposed solution.
Section IV shows the results of the proposed solution.
Section V outlines the conclusions.
2. PROBLEM DEFINITION
Image segmentation is mainly based on the properties of the
image like discontinuity and similarity which further depend
on the gray, color or texture properties. Section I clearly
elaborates the existing techniques used for image
segmentation. The result of segmentation must be
appropriate for any image processing system such that the
classifier has to classify the image as a class based on the
features that are extracted from the segmented image. In any
medical image system, segmentation plays a prime role as
all components of the image may not be related to be useful
for classification. Hence it is quite obvious and necessary to
have a better segmentation such that only the region of
interest need to be extracted. Section II clearly defines the
solution to extract such region of interest.
3. PROPOSED SOLUTION
Section II elaborated the need for segmentation in a medical
image retrieval system. For proper segmentation of an image
it is necessary to enhance the image first and then apply any
of the segmentation techniques. The raw image that is
acquired will be of noisy in nature and it need to be
enhanced. Hence it is proposed to enhance the image using
adaptive histogram equalization and then follow the
segmentation process.
There exists various segmentation techniques[10] out of
which thresholding is a very old, simple technique which
resolves most of the problems. It is proposed to use manual
thresholding based on the gray level values of the image.
The technique proposed computes the threshold manually by
locating the pixel values that are irrelevant to the current
process of segmentation. The pixels values which are having
a gray level within the prescribed limits are retained and the
remaining are discarded. It is clearly identified from the
thresholding process that only the regions that are malignant
and benign are only visible and normal cases are not at all
visible after this stage of processing.
After thresholding a novel algorithm is proposed for
segmentation[9] by selecting an edge point and removing
the point on the edge by computing the neighborhood in the
divided search area of size 3 x 3. If no neighboring pixels is
located, then the point is considered as an isolated point and
removed from the edge pixels list. In this way all the pixels
of the image are marked.
The result of this process is the segmented image. But the
aim is to segment only the tumor part and not the remaining
parts. Hence it is proposed to apply morphological image
processing techniques to extract the tumor from the brain
MRI image. It is observed that the proposed segmentation
has resulted in better segmentation over the existing
techniques.
In order to automate the process of classification it is
proposed to extract various features[9] from the segmented
image and are classified using Decision Tree Classifier[9].
The next section clearly explains the result of segmentation
and classification process.
4. RESULTS
Fig – 2 shows the database that is used for this research. In
this research it is proposed to consider all types of tumor
images which contain malignant, benign and normal case.
Fig - 2. Database used for this work.](https://image.slidesharecdn.com/anovelmedicalimagesegmentationandclassificationusingcombinedfeaturesetanddecisiontreeclassifier-160916050834/85/A-novel-medical-image-segmentation-and-classification-using-combined-feature-set-and-decision-tree-classifier-2-320.jpg)
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 09 | September-2015, Available @ http://www.ijret.org 85
Fig - 3. Original Image
Fig - 4. Result of Thresholding
Fig – 5. Result of Segmentation
The original image taken for segmentation is showin in Fig.
– 3. Fig. – 4 shows the result of thresholding proposed prior
to segmentation. As discussed earlier the proposed
segmentation has resulted in optimal segmentation when
compared to the conventional techniques and the result of
segmentation of the tumor is shown in Fig. – 5. It is
observed that the proposed segmentation has resulted in
70% accurate segmentation. Further various texture based
features [9] are computed from the extracted tumor and are
saved in the database for further testing.
The system is tested with the saved feature set and in this
work it is proposed to use decision tree classifier for testing.
A decision tree can be expressed as a repetitive partition of
the given data space. A decision tree consists of a rooted
tree which will be directed with a node called root, which is
prime and remaining nodes are leaves. Decision tree
algorithm constructs a decision tree for the given dataset
automatically such that the error is minimal. Decision tree
classifier tries to optimize the cost function to find a
decision tree T with a given set of L labeled samples. Here it
tries to optimize the decision tree and find an optimal class
out of the given dataset when a query image is given as a
test case[9].
When the system is tested with the proper segmented results
it is observed that the proposed technique has achieved 94%
accuracy.
5. CONCLUSIONS
Automated medical image diagnosis requires proper
segmentation for further processing and identification. In
this work a novel segmentation based on morphological
processing and identification of the tumor images based on
thresholding is proposed. It is observed that the system has
resulted in 70% accurate segmentation and using the
features that are extracted from the segmented tumor image,
the test results are promising and an accuracy of 94% is
achieved.
REFERENCES
[1] P. Ghosh, S.Antani, L. R. Long, G.R.Thoma, "Review of
medical image retrieval systems and future directions", 2011
24th International Symposium on Computer-Based Medical
Systems (CBMS), June 2011, pp. 1-6.
[2] Zhu L, Appia V, Yezzi A, Arepalli C, Faber T, Stillman
A, et al. Automatic delineation of the myocardial wall from
CT images via shape segmentation and variational region
growing. IEEE Trans Biomed Eng 2013;60:2887-95.
[3] Ankit, A. ; Mandal, M.,"Novel hybrid hardware
architecture for nuclei detection in skin histopathological
images", 2015 IEEE International Conference on Signal
Processing, Informatics, Communication and Energy
Systems (SPICES), Feb. 2015, pp. 1-6.
[4] Ayerdi, B.; Maiora, J. ; Grana, M., "Active learning of
Hybrid Extreme Rotation Forests for CTA image
segmentation", 2012 12th International Conference on
Hybrid Intelligent Systems (HIS), Dec. 2012, pp. 543-548.](https://image.slidesharecdn.com/anovelmedicalimagesegmentationandclassificationusingcombinedfeaturesetanddecisiontreeclassifier-160916050834/85/A-novel-medical-image-segmentation-and-classification-using-combined-feature-set-and-decision-tree-classifier-3-320.jpg)
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 09 | September-2015, Available @ http://www.ijret.org 86
[5] Yijun Chen, "Microscopic Image Segementing and
Classification with RBF Neural Network", 2012
International Symposium on Information Science and
Engineering (ISISE), Dec. 2012, pp. 311-314.
[6] Theodosiou, Z. ; Kasapi, C. ; Tsapatsoulis, N.,
"Semantic Gap between People: An Experimental
Investigation Based on Image Annotation", 2012 Seventh
International Workshop on Semantic and Social Media
Adaptation and Personalization (SMAP), Dec. 2012, pp. 73-
77.
[7] Anh Nguyen, Bac Le, "3D point cloud segmentation: A
survey", 2013 6th IEEE Conference on Robotics,
Automation and Mechatronics (RAM), Nov. 2013, pp. 225-
230.
[8] Farulla, G.A.;Indaco, M. ; Rolfo, D. ; Russo, L.O. ;
Trotta, P., "Evaluation of image deblurring algorithms for
real-time applications", 2014 9th IEEE International
Conference On Design & Technology of Integrated Systems
In Nanoscale Era (DTIS), May 2014, pp. 1-6.
[9] Satyanarayana Tallapragada, V. V. ;Rajan, E.G.;
"Morphology based non ideal iris recognition using decision
tree classifier", 2015 International Conference on Pervasive
Computing (ICPC), Jan. 2015, pp. 1-4.
[10] K. K. Rahini, S.S.Sudha, "Review of Image
Segmentation Techniques: A Survey", International Journal
of Advanced Research in Computer Science and Software
Engineering, Vol. 4, Issue 7, July, 2014, pp.842-845.
[11] Pratibha Thakur, Nishi Madaan2, "A Survey of Image
Segmentation Techniques", International Journal of
Research in Computer Applications and Robotics, Vol.2,
Issue 4, April 2014, pp. 158-165.
[12] N. Senthilkumaran, R. Rajesh, "Edge Detection
Techniques for Image
Segmentation – A Survey of Soft Computing Approaches",
International Journal of Recent Trends in Engineering, Vol.
1, No. 2, May 2009, pp. 250-254.
[13] Savita Agrawal, deepak Kumar Xaxa, "Survey of
Image Segmentation Techniques and Color Model",
International Journal of Computer Science and Information
Technologies, Vol. 5 (3) , 2014, pp. 3025-3030 .
[14] R. Haralick, “Image segmentation survey,” in
Fundamentals in Computer Vision, O. Faugeras, Ed.
Cambridge, MA: Cambridge Univ. Press, 1983, pp. 209–
224.
[15] Muhammad Waseem Khan, "A Survey: Image
Segmentation Techniques ", International Journal of Future
Computer and Communication, Vol. 3, No. 2, April 2014,
pp. 89-93
[16] Waseem Khan, "Image Segmentation Techniques: A
Survey", Journal of Image and Graphics Vol. 1, No. 4,
December 2013, pp. 166-170.](https://image.slidesharecdn.com/anovelmedicalimagesegmentationandclassificationusingcombinedfeaturesetanddecisiontreeclassifier-160916050834/85/A-novel-medical-image-segmentation-and-classification-using-combined-feature-set-and-decision-tree-classifier-4-320.jpg)
A novel medical image segmentation and classification using combined feature set and decision tree classifier
- 1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 04 Issue: 09 | September-2015, Available @ http://www.ijret.org 83 A NOVEL MEDICAL IMAGE SEGMENTATION AND CLASSIFICATION USING COMBINED FEATURE SET AND DECISION TREE CLASSIFIER V. V. Satyanarayana Tallapragada1 , D. Manoj Reddy2 , P. Shashi Kiran3 , D. Venkat Reddy4 1 Associate Professor, Department of ECE, Matrusri Engineering College, Hyderabad, India. [email protected] 2 B.E. IV/IV Student, Department of ECE, MGIT, Hyderabad, India. [email protected] 3 B.E. IV/IV Student, Department of ECE, MGIT, Hyderabad, India. [email protected] 4 Professor, Department of ECE, MGIT, Hyderabad, India. [email protected] Abstract Diagnosis is the first step before giving a medicine to the patient. In the recent past such diagnosis is performed using medical images where segmentation is the prime part in the medical image retrieval which improves the feature set that is collected from the segmented image. In this paper, it is proposed to segment the medical image a semi decision algorithm that can segment only the tumor part from the CT image. Further texture based techniques are used to extract the feature vector from the segmented region of interest. Medical images under test are classified using decision tree classifier. Results show better performance in terms of accuracy when compared to the conventional methods. Key Words: Medical Images, Decision Tree Classifier, Segmentation, Semi-decision algorithm --------------------------------------------------------------------***---------------------------------------------------------------------- 1. INTRODUCTION With the rapid increase in the population there is a need for development of fast diagnosis in the field of medicine. Thus an efficient and more effective method is required for better diagnosis of medical image information. Segmentation is the prime part in the automatic diagnosis of a disease where a system is given an input such that it segments the input image and further diagnoses based on the intelligence that is provided to it. Figure 1 shows the basic block diagram of a medical diagnosis system. Fig - 1. Basic Block Diagram of a medical diagnosis system. The basic problem with such a system is the segmentation process. Optimal algorithms need to be formulated for proper segmentation of the region of interest such that features can be extracted from the region of interest. Further these features must be selected in such a way that they do not pose any classification problems. Content based image retrieval has gained much importance in the recent past and this is applied over medical image where based on the content of the medical image, similar images are retrieved from the database. Several ways were determined to improve the relevance of multi-modal information retreival in medical system that are being developed. There are several medical information retreival systems. An attempt is made to get the metadata about the image from the full text of the subject and retreive the similar images in response to that of the query image is provided[1]. Further a step forward in the diagnosis is done which extracts the mycardial wall of the left and right ventricles from cardiac CT images. The ventricles are primarily located and then detected by first identifying the endocardium and then segmenting it by locating it using active contour model. This method proved to be robust and accurate[2]. In order to analyse skin histopathologial images for the diagnosis of skin cancer various computer aided automated diagnosis techniques are developed. To improve the speed fo the technique a FPGA based hybrid implementation framework is developed[3]. Existing segmentation techniques are used for segmenting only 2D
- 2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 04 Issue: 09 | September-2015, Available @ http://www.ijret.org 84 images. Hybrid Extreme Rotation Forest classifier is developed for segmenting 3D CTA images which is a group of classifier composing of extreme learning machine and decision trees[4].Biomedical research has been extended by segmenting the interested objects from the microscopic images and classifying them. It is achieved using RBF network combined with fuzzy and graph based discrete approach[5]. With the rapid increase in the number of images that are stored in the database there is a problem with image annotation which cannot be manually done. Hence it is the problem which is still pertinent and remained despite the development of many content based multimedia retrieval solutions[6]. The problem becomes more worse as the images that are store may be homogeneous in nature. Hence a technique was proposed which segments using 3D points[7]. Further various algorithms exists which can improve the contrast of the image as the images that are acquired are of low quality in nature which need to be enhanced for further processing[8]. With the technological advancement security lapse is of major concern. Hence different techniques are adopted to provide better security. In this juncture, biometrics is widely used. Iris is one of such biometric which can provide high security when compared to other existing biometric traits. A novel segmentation method is developed for segmenting the iris part which is occluded and can be seen partially[9]. Segmentation is primarily based on partitioning an image based on the changes in the regions which is nothing but based on the content of the image[10-11]. Edge in an image which enmark the boundaries between two regions. Edge based segmentation are used to detect the sharp discontinuties in an image. Further such a edge based segmentation can also be extended using various color models[12-16]. This paper is organized in five sections. Section I introduces the topic. Section. II clearly delineates the problem definition and Section III elaborates the proposed solution. Section IV shows the results of the proposed solution. Section V outlines the conclusions. 2. PROBLEM DEFINITION Image segmentation is mainly based on the properties of the image like discontinuity and similarity which further depend on the gray, color or texture properties. Section I clearly elaborates the existing techniques used for image segmentation. The result of segmentation must be appropriate for any image processing system such that the classifier has to classify the image as a class based on the features that are extracted from the segmented image. In any medical image system, segmentation plays a prime role as all components of the image may not be related to be useful for classification. Hence it is quite obvious and necessary to have a better segmentation such that only the region of interest need to be extracted. Section II clearly defines the solution to extract such region of interest. 3. PROPOSED SOLUTION Section II elaborated the need for segmentation in a medical image retrieval system. For proper segmentation of an image it is necessary to enhance the image first and then apply any of the segmentation techniques. The raw image that is acquired will be of noisy in nature and it need to be enhanced. Hence it is proposed to enhance the image using adaptive histogram equalization and then follow the segmentation process. There exists various segmentation techniques[10] out of which thresholding is a very old, simple technique which resolves most of the problems. It is proposed to use manual thresholding based on the gray level values of the image. The technique proposed computes the threshold manually by locating the pixel values that are irrelevant to the current process of segmentation. The pixels values which are having a gray level within the prescribed limits are retained and the remaining are discarded. It is clearly identified from the thresholding process that only the regions that are malignant and benign are only visible and normal cases are not at all visible after this stage of processing. After thresholding a novel algorithm is proposed for segmentation[9] by selecting an edge point and removing the point on the edge by computing the neighborhood in the divided search area of size 3 x 3. If no neighboring pixels is located, then the point is considered as an isolated point and removed from the edge pixels list. In this way all the pixels of the image are marked. The result of this process is the segmented image. But the aim is to segment only the tumor part and not the remaining parts. Hence it is proposed to apply morphological image processing techniques to extract the tumor from the brain MRI image. It is observed that the proposed segmentation has resulted in better segmentation over the existing techniques. In order to automate the process of classification it is proposed to extract various features[9] from the segmented image and are classified using Decision Tree Classifier[9]. The next section clearly explains the result of segmentation and classification process. 4. RESULTS Fig – 2 shows the database that is used for this research. In this research it is proposed to consider all types of tumor images which contain malignant, benign and normal case. Fig - 2. Database used for this work.
- 3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 04 Issue: 09 | September-2015, Available @ http://www.ijret.org 85 Fig - 3. Original Image Fig - 4. Result of Thresholding Fig – 5. Result of Segmentation The original image taken for segmentation is showin in Fig. – 3. Fig. – 4 shows the result of thresholding proposed prior to segmentation. As discussed earlier the proposed segmentation has resulted in optimal segmentation when compared to the conventional techniques and the result of segmentation of the tumor is shown in Fig. – 5. It is observed that the proposed segmentation has resulted in 70% accurate segmentation. Further various texture based features [9] are computed from the extracted tumor and are saved in the database for further testing. The system is tested with the saved feature set and in this work it is proposed to use decision tree classifier for testing. A decision tree can be expressed as a repetitive partition of the given data space. A decision tree consists of a rooted tree which will be directed with a node called root, which is prime and remaining nodes are leaves. Decision tree algorithm constructs a decision tree for the given dataset automatically such that the error is minimal. Decision tree classifier tries to optimize the cost function to find a decision tree T with a given set of L labeled samples. Here it tries to optimize the decision tree and find an optimal class out of the given dataset when a query image is given as a test case[9]. When the system is tested with the proper segmented results it is observed that the proposed technique has achieved 94% accuracy. 5. CONCLUSIONS Automated medical image diagnosis requires proper segmentation for further processing and identification. In this work a novel segmentation based on morphological processing and identification of the tumor images based on thresholding is proposed. It is observed that the system has resulted in 70% accurate segmentation and using the features that are extracted from the segmented tumor image, the test results are promising and an accuracy of 94% is achieved. REFERENCES [1] P. Ghosh, S.Antani, L. R. Long, G.R.Thoma, "Review of medical image retrieval systems and future directions", 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), June 2011, pp. 1-6. [2] Zhu L, Appia V, Yezzi A, Arepalli C, Faber T, Stillman A, et al. Automatic delineation of the myocardial wall from CT images via shape segmentation and variational region growing. IEEE Trans Biomed Eng 2013;60:2887-95. [3] Ankit, A. ; Mandal, M.,"Novel hybrid hardware architecture for nuclei detection in skin histopathological images", 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Feb. 2015, pp. 1-6. [4] Ayerdi, B.; Maiora, J. ; Grana, M., "Active learning of Hybrid Extreme Rotation Forests for CTA image segmentation", 2012 12th International Conference on Hybrid Intelligent Systems (HIS), Dec. 2012, pp. 543-548.
- 4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 04 Issue: 09 | September-2015, Available @ http://www.ijret.org 86 [5] Yijun Chen, "Microscopic Image Segementing and Classification with RBF Neural Network", 2012 International Symposium on Information Science and Engineering (ISISE), Dec. 2012, pp. 311-314. [6] Theodosiou, Z. ; Kasapi, C. ; Tsapatsoulis, N., "Semantic Gap between People: An Experimental Investigation Based on Image Annotation", 2012 Seventh International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), Dec. 2012, pp. 73- 77. [7] Anh Nguyen, Bac Le, "3D point cloud segmentation: A survey", 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), Nov. 2013, pp. 225- 230. [8] Farulla, G.A.;Indaco, M. ; Rolfo, D. ; Russo, L.O. ; Trotta, P., "Evaluation of image deblurring algorithms for real-time applications", 2014 9th IEEE International Conference On Design & Technology of Integrated Systems In Nanoscale Era (DTIS), May 2014, pp. 1-6. [9] Satyanarayana Tallapragada, V. V. ;Rajan, E.G.; "Morphology based non ideal iris recognition using decision tree classifier", 2015 International Conference on Pervasive Computing (ICPC), Jan. 2015, pp. 1-4. [10] K. K. Rahini, S.S.Sudha, "Review of Image Segmentation Techniques: A Survey", International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue 7, July, 2014, pp.842-845. [11] Pratibha Thakur, Nishi Madaan2, "A Survey of Image Segmentation Techniques", International Journal of Research in Computer Applications and Robotics, Vol.2, Issue 4, April 2014, pp. 158-165. [12] N. Senthilkumaran, R. Rajesh, "Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches", International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009, pp. 250-254. [13] Savita Agrawal, deepak Kumar Xaxa, "Survey of Image Segmentation Techniques and Color Model", International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, pp. 3025-3030 . [14] R. Haralick, “Image segmentation survey,” in Fundamentals in Computer Vision, O. Faugeras, Ed. Cambridge, MA: Cambridge Univ. Press, 1983, pp. 209– 224. [15] Muhammad Waseem Khan, "A Survey: Image Segmentation Techniques ", International Journal of Future Computer and Communication, Vol. 3, No. 2, April 2014, pp. 89-93 [16] Waseem Khan, "Image Segmentation Techniques: A Survey", Journal of Image and Graphics Vol. 1, No. 4, December 2013, pp. 166-170.