Tutorial 9: Diagnosing Image Quality

Before building a pipeline, it helps to assess the quality of your plate images. PhenoTypic’s diagnostics plotter gives you objective metrics for noise, contrast, and structure — so you can make informed decisions about which enhancers and detectors to use.

What you will learn:

1. Use image.plot.diagnostics() to assess plate quality

2. Interpret the noise, contrast, and structure metrics

3. Use quality metrics to guide pipeline design

Imports

from phenotypic.data import load_yeast_plate
import matplotlib.pyplot as plt  # diagnostics() returns matplotlib figures

Load the Plate

plate = load_yeast_plate()
plate.dash()

Data type cannot be displayed: application/vnd.plotly.v1+json

Run Diagnostics

The .plot.diagnostics() method produces a multi-panel figure and returns a dictionary of quantitative metrics. This is one place where matplotlib output is expected — the diagnostics figure is a static multi-panel layout.

fig, metrics = plate.plot.diagnostics()

Inspect the Metrics

The metrics dictionary contains objective measurements organized by category. Let’s look at each one.

print("Available metric categories:")
for category in metrics:
    print(f"  {category}")

Available metric categories:
  bit_depth
  noise
  contrast
  structure
  background
  quality_scores
  interpretations
  recommendations

Noise Metrics

Noise metrics tell you how much random variation exists in the image background. High noise can confuse detectors.

if "noise" in metrics:
    print("Noise metrics:")
    for key, val in metrics["noise"].items():
        if isinstance(val, (int, float)):
            print(f"  {key}: {val:.4f}")
        else:
            print(f"  {key}: {val}")

Noise metrics:
  snr: 16.8730
  sigma_mad: 0.0197
  correlation_length: 49.5000

• SNR (Signal-to-Noise Ratio) — higher is better. Values below 10 suggest the image would benefit from denoising (StableDenoise or GaussianBlur).

• Correlation length — longer correlation suggests structured noise (e.g., uneven illumination) rather than random pixel noise.

Contrast Metrics

Contrast metrics measure how well colonies separate from the agar background.

if "contrast" in metrics:
    print("Contrast metrics:")
    for key, val in metrics["contrast"].items():
        if isinstance(val, (int, float)):
            print(f"  {key}: {val:.4f}")
        else:
            print(f"  {key}: {val}")

Contrast metrics:
  rms_contrast: 0.2567
  michelson: 0.4287
  dynamic_range: 0.0022
  p1: 0.2520
  p99: 0.6302

• RMS contrast — overall contrast level. Low values mean faint colonies that may need CLAHE to boost local contrast.

• Michelson contrast — ratio of (max − min) / (max + min). Values close to 1.0 indicate strong colony/agar separation.

• Dynamic range — fraction of the bit depth in use. Low dynamic range suggests the image is under-exposed.

Structure Metrics

Structure metrics assess the spatial organization of the image.

if "structure" in metrics:
    print("Structure metrics:")
    for key, val in metrics["structure"].items():
        if isinstance(val, (int, float)):
            print(f"  {key}: {val:.4f}")
        else:
            print(f"  {key}: {val}")

Structure metrics:
  mean_coherence: 0.2913
  optimal_scale: 1.0000
  peak_response: 0.0756
  ridge_responses: [0.05793954400883872, 0.07563562324701593, 0.06950456032556798, 0.04796234141535493, 0.03477529722230368, 0.03136497550995987, 0.02972701604558307]
  scales: [0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0]
  ridge_method: meijering

• Gradient mean — average edge strength. Higher values mean sharper colony boundaries, which makes detection easier.

• Coherence — consistency of edge orientation. High coherence on grid plates suggests well-organized colonies.

Other Plot Methods

The .plot accessor offers additional visualization tools beyond diagnostics. Here are a few useful ones:

• ``plate.plot.all()`` — side-by-side view of RGB, grayscale, detect_mat, and masks

• ``plate.plot.size_distribution()`` — histogram of colony sizes (requires detection)

• ``plate.plot.morph_progression()`` — morphological operations at increasing scales

• ``plate.plot.try_thresh()`` — visual comparison of threshold methods

plt.close("all")

Summary

You now know how to assess plate image quality before committing to a pipeline:

• ``plate.plot.diagnostics()`` — multi-panel figure + metrics dictionary

• Noise metrics guide denoising decisions (SNR, correlation length)

• Contrast metrics guide enhancement decisions (RMS contrast, dynamic range)

• Structure metrics assess colony edge quality (gradient, coherence)

Use these metrics to choose between enhancers, detectors, and prefab pipelines — rather than guessing.

Next up: Tutorial 10: Detecting Filamentous Fungi — handle branching fungal morphology with PhenoTypic’s specialized detector.