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  • 1. L.T.D
    Lung Tumors Diagnosis Aiding system
    using CBIR
    1
  • 2. Supervisors
    • Prof.Dr.Mostafa Gad El Haq.
    • 3. Dr.Safwat Hamad.
    • 4. T.A. Noha Ali.
    • 5. T.A. AmrGamgoum.
    2
  • 6. Teamwork
    Ahd Abd EL-Razek Mustafa.
    Reham Mohammad Kamal.
    Fatma Mohammad Samy.
    3
  • 7. Agenda
    Introduction.
    Problem definition.
    Objective.
    System overview.
    Time plan.
    Tools.
    References.
    4
  • 8. Introduction
    Content-based image retrieval (CBIR) is the digital image searching problem in large databases that makes use of the contents of the images .
    5
  • 9. Rather than relying on manual indexing and text description for every image, low-level visual features automatically extracted are used for representation of the image content.
    6
    Introduction
  • 10. The medical domain is often cited as one of the principal application domains for content based access technologies.
    this technology is still in the early development stage. Much research work is needed before the CBIR-based CAD schemes can be accepted in the clinical practice.
    7
    Introduction
  • 11. Problem definition
    In the clinical practice physicians decide on a diagnosis by visually comparing the case at hand with previously published cases in the medical literature.
    8
  • 12. Searching for and identifying the similar reference cases (or images) from the large and diverse clinical databases is a quite difficult task.
    9
    Problem definition
  • 13. Currently, the most of available search systems developed and implemented in medical informatics and picture archiving use TBIR(Text Based Image Retrieval) schemes that are based on the annotated textual information to select similar or clinically relevant cases .
    10
    Problem definition
  • 14. This approach is typically limited to retrieve or select the same type of medical images (i.e., mammograms or CT brain images).
    However, the relevant clinical information depicted on medical images is locally presented (i.e., breast masses depicted on mammograms and emphysema lesions depicted on lung CT images).
    11
    Problem definition
  • 15. Since the nature of the queried suspicious regions is often un-determined, the CBIR is the only available and reliable approach to retrieve the clinically relevant (reference) cases along with the proven pathology and other related clinical information.
    As a result, developing CBIR schemes has been attracting extensive research interest in the areas of medical informatics for the last decade.
    12
    Problem definition
  • 16. Objective
    Providing a software system that helps physicians in the diagnosis of lung tumors using the CBIR technique.
    By comparing the patient's CT (Computed Tomography) image by the previously saved CTs in the database .
    13
  • 17. Then displaying the images and description of the cases that match with the patient's CT.
    To help the physicians in reaching the right diagnosis.
    14
    Objective
  • 18. System overview
    15
    Queried image
    Depicting the detected or identified suspicious lesions
    A set of the most similar cases with diagnosis.
    User Interface
    Queried seed
    Region growth and segmentation
    Similar images
    Segmented ROI
    Matching algorithm
    Feature extraction and computation
    Images Database
    indexed with feature vector
    Feature
    vector
    Similarity comparison
    15
  • 19. 16
    Time plan
  • 20. Tools
    Matlab 2009b.
    Visual studio 2008.
    SQL server 2008.
    17
  • 21. References
    Bin Zheng - Computer-Aided Diagnosis in Mammography Using Content-Based Image Retrieval Approaches: Current Status and Future Perspectives-2009- http://www.mdpi.com/journal/algorithms
    Alex M. Aisen, MD-Lynn S. Broderick, MD-Helen Winer-Muram, MD-Carla E. Brodley, PhD-Avinash C. Kak, PhD-Christina Pavlopoulou, MS-Jennifer Dy, PhD-Chi-RenShyu, PhD-Alan Marchiori, BS- Automated Storage and Retrieval of Thin-Section CT Images to Assist Diagnosis: System Description and Preliminary Assessment-2003 .
    http://rvl.www.ecn.purdue.edu/RVL/CBIR/CBIROverview.html
    18
  • 22. 19
    Questions
  • 23. 20