Contour Detection

DataLab provides a “Contour Detection” feature which is based on the marching cubes algorithm.

Contour detection parameters.

How to use the feature:

• Create or open an image in DataLab workspace

• Eventually create a ROI around the target area

• Select “Contour detection” in “Analysis” menu

• Enter parameter “Shape” (“Ellipse”, “Circle” or “Polygon”)

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

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

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

  • This feature requires at least 2 detected contours

  • Created ROIs can be useful for subsequent processing on each contour area

Contour detection results (see test “contour_app.py”)

Results are shown in a table:

• Each row is associated to a contour

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

• Other columns show contour coordinates: 4 columns for circles (coordinates of diameter), 8 columns for ellipses (coordinates of diameters)

Example of contour detection.

The contour detection algorithm works in the following way:

• First, iso-valued contours are computed (implementation based on skimage.measure.find_contours.find_contours)

• Then, each contour is fitted to the closest ellipse (or circle)

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