Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation 3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms Volume Identification and Estimation of MRI Brain Tumor MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier

1. Medical Image Analysis
Hossam Mahmoud Moftah and Aboul Ella Hassanien
Cairo University,
Dept. of Information Technology, Faculty of Computers and information
Scientific Research Group in Egypt
http://www.egyptscience.net

2. Agenda
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Introduction
Objectives
Scope of work and proposed solutions
Medical Imaging
Medical Image Segmentation
Ant Colony Optimization
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Proposed Approaches
• Adaptive K-Means Clustering Algorithm for MR Breast Image
Segmentation
• 3D Brain Tumor Segmentation Scheme using K-mean Clustering and
Connected Component Labeling Algorithms
• Volume Identification and Estimation of MRI Brain Tumor
• MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based
segmentation and Multilayer Perceptron NN classifier
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Conclusions and Future Work

3. Introduction
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There are many problems in medical image
analysis and interpretation involve the need
for a computer aided system to understand the
images and image structure and know what it
means.
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Bio-medical Image analysis and processing
has great significance in the field of medicine,
especially in Non-invasive treatment and
clinical study.
The accurate interpretation and analysis of
medical images often become boring and time
consuming, because there is much detail in
such images.

4. Objectives
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Applying information analysis and
visualization to biomedical research
problems.
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Developing computational methods and
algorithms to analyze and quantify
biomedical data.
Developing methods and applications
to give our collaborators the ability to
analyze biomedical data to help in the
discovery of biomedical knowledge and
the diagnosis of diseases.

5. Scope of work and proposed solutions
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In this research complete solutions
proposed for medical image analysis.
are
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Different approaches and systems that
combines the advantages of intelligent
techniques are presented such as:
• Ant-based clustering, k-means clustering and neural
network classifiers, in conjunction with statisticalbased feature extraction.
• Robust medical imaging systems to analyze and
interpret 2D and 3D medical images including 3D
brain tumor, MRI breast images.
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6. Medical Imaging
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Image intensities can be:
• Radiation absorption in X-ray imaging
• Acoustic pressure in ultrasound
• Radio frequency (RF) signal amplitude in
MRI
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Dimensionality: Refers to whether a
segmentation method operates in a 2-D
image domain or a 3-D image domain.
Generally, 2-D methods are applied to 2D images, and 3-D methods are applied
to 3-D images.

7. Medical Imaging
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In some cases, however, 2-D methods
are applied sequentially to the slices of a
3-D image.
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This may arise because of practical
reasons:
• Ease of implementation
• Lower computational complexity
• Reduced memory requirements
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Major Modalities:
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Projection X-ray (Radiography)
Computed Tomography (CT)
Magnetic Resonance Imaging (MRI)
Ultrasound

8. MRI vs. CT Scan
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CT scans are a specialized type of x-ray
MRI uses a magnetic field with radio
frequencies introduced into it.

9. MRI vs. CT Scan
CT
CT is not the best choice for that
MRI
MRI Scan show tendons and ligaments
very well
Bleeding in the brain, especially from MRI is not the best choice for that
injury
CT is not the best choice for that
Tumor in the brain is better seen on MRI
bone structures - the inner ears - can MRI is not the best choice for that
easily detect tumors within the auditory
canals
CT shows organ tear and organ injury MRI is not the best choice for that
quickly and efficiently for the damaged
organs or torn in accident
Broken bones and vertebral bodies of MRI is not the best choice for that
the spine
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MRI is not the best choice for that
Injury to the spinal cord itself

10. MRI vs. CT Scan
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11. Image Segmentation
• Image segmentation is the task of splitting a
digital image into one or more regions of
interest.
• Image Segmentation Techniques:
• Region-based segmentation
• Data clustering
• Edge-base segmentation
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12. Image Segmentation
• Region-based segmentation
• Seeded Region Growing
• Region Splitting and Merging
• Data clustering
• Hierarchical
• Hierarchical divisive algorithm
• Partitional
• K-means Clustering Algorithm
• Edge-base segmentation
• Watershed Segmentation Algorithm
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13. Ant Colony Optimization
• Ant Colony Optimization is an
efficient method to finding
optimal solutions to a graph
• Using three algorithms based on
choosing
a
city,
updating
pheromone trails and pheromone
trail decay, we can determine an
optimal solution to a graph
• Ant
Colony Optimization has
been used to figure out solutions
to real world problems, such as
truck routing
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14. Proposed Approaches: 2D Segmentation
Adaptive K-Means Clustering Algorithm for
MR Breast Image Segmentation
Hossam M.Moftah, Ahmed Taher Azar, Eiman. T. AlShammari, Neveen. I.Ghali, Aboul Ella Hassanien,
and Mahmoud Shoman, "Adaptive K-Means
Clustering Algorithm for MR Breast Image
Segmentation", Neural Computing and Applications
Journal, DOI 10.1007/s00521-013-1437-4 (Springer),
2013. (Impact factor =1.6).
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15. Adaptive K-Means Clustering Algorithm for MR
Breast Image Segmentation
• The most popular method for clustering is k-means
clustering.
• This article presents a new approach intended to provide
more reliable magnetic resonance (MR) breast image
segmentation that is based on adaptation to identify
target objects through an optimization methodology that
maintains the optimum result during iterations.
• The proposed approach improves and enhances the
effectiveness and efficiency of the traditional k-means
clustering algorithm.
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16. K-means Algorithm
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17. Block diagram of the proposed approach
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18. Adaptive k-mean segmentation approach
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19. Experimental results and comparative analysis
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20. Experimental results and comparative analysis
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21. Proposed Approaches: 3D Segmentation
3D Brain Tumor Segmentation Scheme using
K-mean
Clustering
and
Connected
Component Labeling Algorithms
Hossam M. Moftah, Aboul Ella Hassanien,
Mohamoud Shoman: 3D brain tumor segmentation
scheme using K-mean clustering and connected
component labeling algorithms. IEEE International
conf, on Intelligent system and design applications
ISDA 2010: 320-324.
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22. 3D Brain Tumor Segmentation Scheme using Kmean Clustering and Connected Component
Labeling Algorithms
• In this article, an image segmentation scheme is proposed
to segment 3D brain tumor from MRI images through the
clustering process.
• The clustering is achieved using K-means algorithm in
conjunction with the connected component labeling
algorithm to link the similar clustered objects in all 2D
slices and then obtain 3D segmented tissue using the
patch object rendering process.
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23. Block diagram of the proposed approach
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24. Clustering and relabeling
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25. 3D Tumor segmentation algorithm
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26. Experimental results
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27. Proposed Approaches: 3D Analysis
Volume Identification and Estimation of
MRI Brain Tumor
Hossam M. Moftah, Neveen I. Ghali, Aboul Ella
Hassanien,
Mahmoud
A.
Ismail:
Volume
identification and estimation of MRI brain tumor.
IEEE Hybrid Intelligent system (HIS 2012): pp. 120124.
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28. Volume Identification and Estimation of MRI Brain
Tumor
• This
article deals with two dimensional magnetic
resonance imaging (MRI) sequence of brain slices which
include many objects to identify and estimate the volume
of the brain tumors.
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29. Volume measurement: an illustrated example
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30. Volume estimation
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31. Experimental results
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32. Proposed Approaches: 3D Analysis
MRI Breast cancer diagnosis hybrid
approach
using
adaptive
Ant-based
segmentation and Multilayer Perceptron NN
classifier
Hossam M. Moftah, Ahmed Taher Azar, Aboul Ella
Hassanien and Mahmoud Shoman, MRI Breast cancer
diagnosis approach using adaptive Ant-based
segmentation and Multilayer Perceptron NN
classifier. Applied Soft Computing Journal (Elsevier),
2013. (Accepted Impact factor = 2.5).
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33. MRI Breast cancer diagnosis hybrid approach using
adaptive Ant-based segmentation and Multilayer
Perceptron NN classifier
• This article introduces a hybrid approach that combines the
advantages of fuzzy sets, ant-based clustering and Multilayer
Perceptron Neural Network (NN) classifier, in conjunction
with statistical-based feature extraction technique.
• An application of breast cancer MRI imaging has been chosen
and hybridization system has been applied to see their ability
and accuracy to classify the breast cancer images into two
outcomes: Benign or Malignant
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34. Block diagram of the proposed approach
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35. Adaptive Ant-based segmentation algorithm
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36. Experimental results and comparative analysis
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37. Experimental results and comparative analysis
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38. Experimental results and comparative analysis
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39. Experimental results and comparative analysis
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40. Conclusions
• Bio-medical image analysis solutions and systems are presented in
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this thesis.
An efficient 2D and 3D segmentation algorithms for medical images
are presented to solve medical image segmentation problems.
A new approach is presented intended to provide more reliable MR
breast image segmentation.
An image segmentation scheme is presented to segment 3D brain
tumor from MRI images.
3D brain tumor identification and volume measurement algorithm
is presented for brain MRI.
A hybrid approach is presented that combines the advantages of
fuzzy sets, ant-based clustering and Multilayer Perceptron Neural
Network (NN) classifier, in conjunction with statistical-based
feature extraction technique.

41. Future Work
• Introducing techniques and algorithms to solve the traditional
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medical problems in my country Egypt such as the early detection
of liver fibrosis stages and other medical problems.
Overcoming limitations inherent in conventional computer-aided
diagnosis, and try to apply intelligent methods and algorithms such
as rough sets, near sets to present an effective method of dealing
with uncertainties.
Introducing and applying optimization techniques to solve medical
problems such as multi-objective optimization or pareto
optimization.
Introducing new versions of optimization algorithms such as
Discrete Invasive Weed Optimization (IWO) algorithm inspired
from weed colonization to solve medical imaging problems.
Dealing with animal medical images to solve medical animal
problems such as mammary gland tumors in cats.
Searching for different new machine learning techniques to optimize
the classification step.

42. Thanks
http://www.egyptscience.net
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