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Automated spot detection

bigfish.detection

Functions used to detect spots in a 2D or 3D image. Detection is performed in three steps:

  1. Image is denoised and spots are enhanced by using a Laplacian of Gaussian (LoG) filter.
  2. Peaks are detected in the filtered image with a local maximum detection algorithm.
  3. An intensity threshold is applied to discriminate actual spots from noisy background.

Detect spots

The main function for spot detection is:

  • bigfish.detection.detect_spots

It is also possible to perform the main steps of the spot detection separately:

  • bigfish.detection.local_maximum_detection
  • bigfish.detection.spots_thresholding

See an example of application here <https://github.com/fish-quant/big-fish- examples/blob/master/notebooks/5%20-%20Detect%20spots.ipynb>.

detect_spots

local_maximum_detection

spots_thresholding

Find a threshold (automatically)

The need to set an appropriate threshold for each image is a real bottleneck that limits the possibility to scale a spot detection. Our method includes a heuristic function to to automatically set this threshold:

  • bigfish.detection.automated_threshold_setting
  • bigfish.detection.get_breaking_point
  • bigfish.detection.get_elbow_values

automated_threshold_setting

get_breaking_point

get_elbow_values

Compute signal-to-noise ratio

bigfish.detection

Compute a signal-to-noise ratio (SNR) for the image, based on the detected spots:

compute_snr_spots