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SWA Segmentation by Weighted Aggregation for Effective Image Analysis

SWA Segmentation by Weighted Aggregation (SWA) is a powerful technique that leverages image segmentation using graph couplings based on intensity similarity. By minimizing cut coupling and coarsening the minimization problem, SWA efficiently detects salient segments in images, offering faster results than traditional methods. This approach utilizes weighted aggregation, hierarchical graph structures, and specialized lesion detection to produce accurate segmentation results for various applications. With features like average intensities and multiscale measurements, SWA delivers precise image analysis for tasks like identifying lesions in medical imaging.

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SWA Segmentation by Weighted Aggregation for Effective Image Analysis

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  1. Image Segmentation

  2. The Pixel Graph Couplings {Wij} Reflect intensity similarity Low contrast – strong coupling High contrast – weak coupling

  3. Segmentationminimize cut coupling Low-energy cut

  4. Normalized-Cut Measure Minimize:

  5. Coarsening the Minimization Problem

  6. Coarsening – Choosing a Coarse Grid Representative subset

  7. Weighted Aggregation aggregate aggregate

  8. Hierarchical Graph

  9. Hierarchyin SWA

  10. Segmentation by Weighted Aggregation Detects the min-normalized-cut salient segments Linear in # of points (a few dozen operations per point) Orders of magnitude faster than…

  11. Image Segmentation

  12. Use Averages to Modify the Graph

  13. Coarse-Scale Measurements • Average intensities of aggregates

  14. Image Segmentation

  15. Aggregate Measurements • Average intensities • Intensity variances (multiscale) • Direction alignment • Boundary alignment • Average “hair” orientation

  16. SWA

  17. Specialized segmentation:Detecting Lesions Tagged Our results Data: Filippi

  18. Our Algorithm (SWA) Ncuts Isotropic texture SWA

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