Detection Strategies Compared

PhenoTypic provides over 20 detectors, each implementing a different strategy for separating colonies from background. This page compares the major approaches and their failure modes.

Threshold-Based Detectors

Threshold detectors compute a single intensity value that separates foreground (colony) from background (agar). Pixels above or below the threshold are classified as colony.

Otsu’s Method

Finds the threshold that minimizes the weighted sum of within-class variances (equivalently, maximizes between-class variance). Assumes a bimodal histogram — one peak for background, one for colonies [1].

Strengths: Fast, parameter-free, works well on clean plates.

Failure mode: When the histogram is not bimodal (e.g., few small colonies on a large plate), the threshold drifts toward the dominant peak and misses the minority class.

Triangle Method

Draws a line from the histogram peak to the tail and finds the point of maximum perpendicular distance. Effective when one class greatly outnumbers the other [2].

Strengths: Robust to class imbalance (few colonies, large background).

Failure mode: Sensitive to histogram smoothness; noise can create spurious peaks.

Hysteresis Thresholding

Uses two thresholds: a high threshold for confident foreground and a low threshold for uncertain regions. Uncertain pixels are included only if connected to confident foreground. This captures soft colony edges that a single threshold misses.

Strengths: Handles gradual intensity transitions at colony boundaries.

Failure mode: Requires good threshold selection; a poor low threshold floods the mask.

Region-Based Detectors

Watershed

Treats the intensity image as a topographic surface and “floods” from local minima. Watershed boundaries separate touching colonies that threshold methods merge into one object.

Strengths: Separates touching and overlapping colonies.

Failure mode: Over-segmentation — small intensity variations create too many regions. Requires careful seed selection or smoothing.

Peak-Based Detectors

RoundPeaksDetector

Designed for grid plates with round colonies. Finds intensity peaks in each grid section, then expands outward to the colony boundary. Assumes one dominant colony per well.

Strengths: Grid-aware, handles variable colony sizes, fast.

Failure mode: Assumes round morphology; fails on irregular or filamentous colonies.

Specialized Detectors

FilamentousFungiDetector

Two-stage detector for branching morphology. First detects inoculation points, then uses phase congruency and minimum-cost pathfinding to reconnect fragmented hyphal branches.

Strengths: Captures thin, branching filaments that threshold methods miss entirely.

Failure mode: Slow on large images; requires BM3D denoising upstream for best results.

CompositeDetector

Combines multiple detectors via union, intersection, or overlap. Useful when no single detector handles all colony types in one plate.

Choosing a Detector

Is the plate a standard 96/384-well grid with round colonies?
  ├── Yes → RoundPeaksDetector
  └── No
      Is the organism filamentous?
        ├── Yes → FilamentousFungiDetector
        └── No
            Are colonies touching?
              ├── Yes → WatershedDetector
              └── No
                  Is the histogram bimodal?
                    ├── Yes → OtsuDetector
                    └── No → TriangleDetector or HysteresisDetector

References

[1] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst., Man, Cybern., vol. 9, no. 1, pp. 62–66, Jan. 1979.

[2] G. W. Zack, W. E. Rogers, and S. A. Latt, “Automatic measurement of sister chromatid exchange frequency,” J. Histochem. Cytochem., vol. 25, no. 7, pp. 741–753, 1977.