Fig. 2

Visualization of main algorithm steps for nuclei and foci identification. The majority of image processing steps were performed in Matlab. The use of ImageJ is explicitly mentioned. a Nuclei image for demonstrating nuclei identification algorithm. b Thresholded blue component of the image (a) in ImageJ by Huang’s method. c Image (b) after filling holes, applying median filter of 3 × 3 size and morphological opening by reconstruction using a disk-shaped structuring element with radius 10. d Image (c) dilated by a 3 × 3 structuring element 3 times. e Image (d) with filled holes. f Image (e) eroded by the 3 × 3 structuring element 3 times. g Watersheding of the image (f) in ImageJ. h Morphological opening of the image (g) using a disk-shaped structuring element with radius 10. The result is a secondary mask. i Applying the secondary mask (h) to the image (a). j Image with the nucleus (blue) and foci (green) for demonstrating foci identification algorithm. k The green component of the image (j). l The 3D format of the image (k). Dimensions x and y indicate pixel positions in the intensity matrix of the foci image and dimension z indicates the pixel intensity value. Pixels belonging to foci have higher intensity than pixels belonging to the background and look like peaks. m Applying the adaptive median filter [22] to the image (k), (l). n Top-hat transformation of the image (m) using a disk-shaped structuring element with the radius r _f  = 3. o H-maxima transformation of the image (n) using the Otsu’s threshold of the image (n) as a parameter. (l ^*-m ^*) Contour plots of images (l-m). p Regional maxima of the image (o). q Applying the mask (p) to the image (n) and thresholding with value T _e  = 0.07. We designated obtained mask as a foci mask. Elements of the foci mask correspond to detected foci. r The original image (j) with identified foci marked by red frames