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Med-LIFE: A System for Medical Imagery Exploration

Med-LIFE: A System for Medical Imagery Exploration Joshua New Erion Hasanbelliu Introduction What is Med-LIFE? What is image fusion? How do I teach the computer? How can I view the results? What is Med-LIFE?

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Med-LIFE: A System for Medical Imagery Exploration

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  1. Med-LIFE:A System for Medical Imagery Exploration Joshua New Erion Hasanbelliu

  2. Introduction • What is Med-LIFE? • What is image fusion? • How do I teach the computer? • How can I view the results?

  3. What is Med-LIFE? • Med-LIFE is an application currently under development to use computer processing techniques to reduce medical personnel workload • GUI designed with QT • Image Processing with C (and VTK library)

  4. What is Med-LIFE? • Consists of Three Processes (LIFE): • Learning of image attributes by the computer using SFAM • Image Fusion of many image modalities into one color image • Exploration of learning and fusion results

  5. What is image fusion? • Allows the combination of multiple image modalities into one colored image with no information loss • Reduces workload by eliminating the number of images a radiologist must analyze • Images used from “The Whole Brain Atlas” • http://www.med.harvard.edu/AANLIB/home.html

  6. What is image fusion? • Technique similar to primate vision

  7. GAD PD Color Fuse Result SPECT T2 Image Fusion Example

  8. Image Fusion Example

  9. How do I teach the computer? • SFAM – Simplified Fuzzy ARTMAP • SFAM is a computer-based system capable of online, incremental learning • Two “vectors” are sent to this system for learning: • Input feature vector tells what data is available from which to learn • Supervisory signal tells whether that vector is an example or counterexample

  10. Main Window Zoom Window How do I teach the computer? • Left-click to define examples (green) • Right-click to define counterexamples (red)

  11. How do I teach the computer? • Supervisory signal from red/green marks • Feature vector from slice pixel values for original, single, and double opponent images

  12. Main Window Results Zoom Window Results Learning Results

  13. Main Window Results T2 Learning Results

  14. How can I view results? • Display a plethora of information • Skull generated for patient from PD modality for contextual slice navigation • Explore tab provides several functions: • Original images • Fusion results imbedded within 3D, patient-generated skull • Learning results

  15. Demo Presentation • Erion will now demo the Med-LIFE system

  16. Conclusion • Med-LIFE offers reduced workload to physicians who scan multiple images • Image processing and fusion reduces the number of images to be analyzed • Learning system allows the computer to perform prescreening or background analysis • Exploration allows immersion within the data for operational planning

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