smi_core.FindROI issue: multiple ROI candidates generated for single emitter #21

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1. dschodt

   assigned

   kalidke

   on Dec 8, 2021
2. kalidke commented on Dec 9, 2021

   

   kalidke

   on Dec 9, 2021

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   The box finder uses the operation pixel==maxpixel from a maximum pixel filter. In rare cases, much more likely in noise-free model data, there can be two identical pixels. Boxes will be returned for both. We should implement a localization combiner algorithm somewhere in LocalizeData.
3. kalidke commented on Nov 4, 2025

   

   kalidke

   on Nov 4, 2025

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   Issue Status: Still Exists (Edge Case Only)

   Verified the issue still exists in current codebase (v1.3.1), but only in noise-free simulation data.

   Test Results

   Test Case	Input	Output	Status
   32×32 noiseless	1 emitter	2 ROIs	✗ Duplicates
   16×16 noiseless	1 emitter	1 ROI	✓ Correct
   3 emitters noiseless	3 emitters	6 ROIs	✗ Duplicates
   32×32 with Poisson noise	1 emitter	1 ROI	✓ Correct

   Root Cause Confirmed

   As noted in the original comment:

   "The box finder uses the operation pixel==maxpixel from a maximum pixel filter. In rare cases, much more likely in noise-free model data, there can be two identical pixels."

   When simulated Gaussian blobs create perfectly symmetric intensity profiles on a discrete pixel grid, multiple pixels can have identical maximum values, causing FindROI to identify multiple candidate positions.

   Why It Doesn't Affect Real Data

   1. Real camera data always has noise (Poisson shot noise, readout noise)
   2. Breaking symmetry: Even small noise breaks the perfect symmetry that causes identical pixel values
   3. Test with Poisson noise passed: Single emitter → single ROI detected

   Recommendation

   Keep open as low-priority enhancement with labels:

   • enhancement - Not a critical bug
   • simulation - Only affects simulation workflows
   • good first issue - Solution is well-defined

   Proposed Solutions

   From original comment: "We should implement a localization combiner algorithm somewhere in LocalizeData."

   Option 1: Post-processing deduplication

   • In LocalizeData.genLocalizations(), merge localizations within distance threshold
   • Check if X, Y positions are identical or within ~0.5 pixels
   • Keep only one, average the parameters

   Option 2: FindROI-level fix

   • After identifying local maxima, check for duplicate positions
   • Remove duplicates before creating ROI boxes
   • More efficient but requires modifying FindROI algorithm

   Option 3: Add jitter to simulations

   • In GaussBlobs.gaussBlobImage(), add small random offset to positions
   • Prevents perfect pixel symmetry in simulations
   • Doesn't fix root cause but prevents issue in testing

   Impact Assessment

   • Severity: Low - doesn't affect real experimental data
   • Frequency: Rare - only noise-free simulations
   • Workaround: Add poissrnd() to simulated data
   • Fix effort: Medium - requires deduplication logic

   The issue is real but has minimal practical impact on actual SMLM workflows.
4. kalidke

   added a commit that references this issue on Nov 4, 2025

   Fix FindROI duplicate localizations from identical pixel maxima (Issue …

   ...

   0f81c72
5. kalidke commented on Nov 4, 2025

   

   kalidke

   on Nov 4, 2025

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   Fix Implemented ✓

   Pull request #52 has been created to fix this issue.

   Solution: Post-Processing Deduplication

   Implemented intelligent duplicate merging in LocalizeData after GaussMLE fitting:

   • New method: mergeDuplicateLocalizations()
   • Location: Called in genLocalizations() after coordinate transformation
   • Logic: Merges localizations < 0.1 pixels apart in same frame, keeping higher LogLikelihood
   • Approach: Post-fix (MATLAB) rather than prevention (CUDA kernel)

   Why Post-Fix?

   • No CUDA recompilation needed
   • Easy to test and maintain
   • Intelligent merging based on fit quality
   • Handles any source of duplicates

   Test Results

   All 5 comprehensive tests pass:

   • ✓ Original bug case (32×32): 2 duplicates → 1 merged
   • ✓ Multiple emitters: 6 duplicates → 3 merged
   • ✓ Preserves distinct emitters (8 pixels apart)
   • ✓ Multi-frame handling works correctly
   • ✓ No false positives on normal data

   Root Cause Confirmed

   When Gaussian blob centered at integer pixel coordinates (e.g., 16.0, 16.0), creates 2×2 block of identical pixel values:

   (15,15) = 77.9090042114
   (15,16) = 77.9090042114  ← IDENTICAL (to 10 decimals)
   (16,15) = 77.9090042114
   (16,16) = 77.9090042114
   

   CUDA kernel tolerance (< 1e-6) flags all four, creating 4 ROI boxes (reduced to 2 after fitting).

   See PR #52 for full implementation details and testing documentation.