2D Peak Detection

DataLab provides a “2D Peak Detection” feature which is based on a minimum-maximum filter algorithm.

2D peak detection parameters.

How to use the feature:

• Create or open an image in DataLab workspace

• Select “2d peak detection” in “Analysis” menu

• Enter parameters “Neighborhoods size” and “Relative threhold”

• Optionally, enable “Create regions of interest” to automatically create ROIs around each detected peak:

  • Choose ROI geometry: “Rectangle” or “Circle”

  • ROI size is automatically calculated based on the minimum distance between detected peaks (to avoid overlap)

  • This feature requires at least 2 detected peaks

  • Created ROIs can be useful for subsequent processing on each peak area (e.g., contour detection, measurements, etc.)

2d peak detection results (see test “peak2d_app.py”)

Results are shown in a table:

• Each row is associated to a detected peak

• First column shows the ROI index (0 if no ROI is defined on input image)

• Second and third columns show peak coordinates

Example of 2D peak detection.

The 2d peak detection algorithm works in the following way:

• First, the minimum and maximum filtered images are computed using a sliding window algorithm with a user-defined size (implementation based on scipy.ndimage.minimum_filter and scipy.ndimage.maximum_filter)

• Then, the difference between the maximum and minimum filtered images is clipped at a user-defined threshold

• Resulting image features are labeled using scipy.ndimage.label

• Peak coordinates are then obtained from labels center

• Duplicates are eventually removed

The 2d peak detection parameters are the following:

• “Neighborhoods size”: size of the sliding window (see above)

• “Relative threshold”: detection threshold

• “Create regions of interest”: if enabled, automatically creates ROIs around each detected peak (requires at least 2 peaks)

• “ROI geometry”: shape of the ROIs (“Rectangle” or “Circle”)

Feature is based on get_2d_peaks_coords function from sigima.tools module: