Medical image analysis

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Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation …

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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  • 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 • • • • • • Introduction Objectives Scope of work and proposed solutions Medical Imaging Medical Image Segmentation Ant Colony Optimization • 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 • 2 Conclusions and Future Work
  • 3. Introduction • • 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. • 3 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 • • Applying information analysis and visualization to biomedical research problems. • 4 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 • In this research complete solutions proposed for medical image analysis. are • 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. 5
  • 6. Medical Imaging • Image intensities can be: • Radiation absorption in X-ray imaging • Acoustic pressure in ultrasound • Radio frequency (RF) signal amplitude in MRI • • 6 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 • In some cases, however, 2-D methods are applied sequentially to the slices of a 3-D image. • This may arise because of practical reasons: • Ease of implementation • Lower computational complexity • Reduced memory requirements • 7 Major Modalities: • • • • Projection X-ray (Radiography) Computed Tomography (CT) Magnetic Resonance Imaging (MRI) Ultrasound
  • 8. MRI vs. CT Scan • • 8 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 9 MRI is not the best choice for that Injury to the spinal cord itself
  • 10. MRI vs. CT Scan 10
  • 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 11
  • 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 12
  • 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 13
  • 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). 14
  • 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. 15
  • 16. K-means Algorithm 16
  • 17. Block diagram of the proposed approach 17
  • 18. Adaptive k-mean segmentation approach 18
  • 19. Experimental results and comparative analysis 19
  • 20. Experimental results and comparative analysis 20
  • 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. 21
  • 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. 22
  • 23. Block diagram of the proposed approach 23
  • 24. Clustering and relabeling 24
  • 25. 3D Tumor segmentation algorithm 25
  • 26. Experimental results 26
  • 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. 27
  • 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. 28
  • 29. Volume measurement: an illustrated example 29
  • 30. Volume estimation 30
  • 31. Experimental results 31
  • 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). 32
  • 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 33
  • 34. Block diagram of the proposed approach 34
  • 35. Adaptive Ant-based segmentation algorithm 35
  • 36. Experimental results and comparative analysis 36
  • 37. Experimental results and comparative analysis 37
  • 38. Experimental results and comparative analysis 38
  • 39. Experimental results and comparative analysis 39
  • 40. Conclusions • Bio-medical image analysis solutions and systems are presented in • • • • • 40 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 • • • • • 41 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 42