Lab 4

Segmentation

Three methods will be compared to segment the coins on the image observation.png: binary thresholding, Otsu’s method, and local thresholding. The methods will be evaluated through the Dice coefficient, by using the ground truth available in image groundtruth.png.

• Apply binary thresholding to the image, by choosing manually the threshold value. Compute the Dice coefficient.

  Note

  Dice coefficient is also known as Sørensen–Dice coefficient or Dice similarity coefficient. Besides, a Dice dissimilarity coefficient also exists: it is equals to \(1-d\) where \(d\) is the Dice similarity coefficient.

  The function scipy.spatial.distance.dice computes the Dice dissimilarity coefficient. Also, it works on boolean 1D arrays, not or 2D arrays. Recall that A.ravel() returns a vectorized version of the array A (see numpy.ndarray.ravel). Besides, A.astype(bool) returns a boolean version of the array A (see numpy.ndarray.astype).

• Use skimage.filters.threshold_otsu to get a threshold value by Otsu’s method and apply the thresholding. Compute the Dice coefficient. What differences do you observe between the two first segmentations? How can these differences be explained?

• Use skimage.filters.threshold_local to perform local thresholding. How works this method? Compute the Dice coefficient.

• Finally, criticize the three methods: identify the good results and the limitations. Suggest improvements.