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M. Aguilar, J. R. New and E. Hasanbelliu Knowledge Systems Laboratory MCIS Department

Advances in the Use of Neurophysiologycally-based Fusion for Visualization and Pattern Recognition of Medical Imagery. M. Aguilar, J. R. New and E. Hasanbelliu Knowledge Systems Laboratory MCIS Department Jacksonville State University Jacksonville, AL 36265. Outline. Introduce Med-LIFE.

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M. Aguilar, J. R. New and E. Hasanbelliu Knowledge Systems Laboratory MCIS Department

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  1. Advances in the Use of Neurophysiologycally-based Fusion for Visualization and Pattern Recognition of Medical Imagery M. Aguilar, J. R. New and E. Hasanbelliu Knowledge Systems Laboratory MCIS Department Jacksonville State University Jacksonville, AL 36265

  2. Outline • Introduce Med-LIFE. • Revisit 3D image fusion architecture. • Compare 2D and 3D fusion results. • Fusion for segmentation and pattern recognition. • Contextual zoom tool. • Segmentation results.

  3. Med-LIFE: Learning, Image Fusion, and Exploration System

  4. 3D Shunt Equation Shunting Neural Network Equation: Grossberg (1968), Elias & Grossberg (1972) Where: A – decay rate B – maximum activation level (set to 1) D – minimum activation level (set to 1) IC – excitatory input IS – lateral inhibitory input C, Gc and Gs are as follows: 3D Shunt Operator Symbol

  5. 2-Band 3D Fusion Architecture

  6. 4-Band 3D Fusion Architecture

  7. 2D Fusion 3D Fusion 2D vs. 3D Fusion Results MRI-T1 MRI-T2 SPECT MRI-PD

  8. T2 Images T1 Images Color Remap . . SPECT Images . . . . Color Fuse Result PD Image + _ 4-Band Hybrid Fusion Architecture Q I Y Noise cleaning & registration if needed Contrast Enhancement Between-band Fusion and Decorrelation

  9. Hybrid Fusion Results 2D Fusion 3D Fusion

  10. User-Driven Learning for Segmentation & Pattern Recognition

  11. Contextual Zoom Visualization • Zoom in place supports: • focused attention • improved screen real-estate usage • Zoom in place: • occludes information • reduces efficiency by forcing user to maintain context

  12. Contextual Zoom Visualization

  13. Contextual Zoom Visualization • Developed based on COTS software developed by Idelix • Supports visualization of fused imagery at multiple details levels • Supports detailed analysis and selection for user-driven pattern learning…

  14. User-Driven Pattern Learning • Supervised learning where training data is selected by user/expert (Waxman et al). • Results assessed and corrected by user. • Fuzzy ARTMAP neural network for fast and stable learning. • Address order sensitivity by introducing N voters trained with alternate ordering of the training data.

  15. Pattern Recognition Results

  16. Heterogeneous Voting • Train 3 Fuzzy ARTMAP systems with parameters as before (different data orderings) • Train remaining 2 systems with all parameters as in the 3rd system except for Vigilance (which is a threshold measure that controls the sensitivity of the system).

  17. Homogeneous vs. Heterogeneous Voters 5 Homogeneous Voters 5 Heterogeneous Voters

  18. 2D Fusion-based Segmentation 3D Fusion-based Segmentation 2D vs. 3D Fusion Segmentation Results

  19. Generalization Training Results Testing Results Slice 10 Slice 11

  20. Conclusions • Modified fusion approach combines benefits of 2D and 3D fusion. • Preliminary learning segmentation results indicate robustness across slices and cases. • Demonstrated superior performance of 3D fusion for both visualization and pattern recognition. • Heterogeneous voting scheme improves learning performance.

  21. BACK-UPS

  22. 2D vs. 3D Generalization 2D Fusion 3D Fusion Slice 10 Testing Results

  23. Image Fusion

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