Bologna University/Image Acquisition and Elaboration

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*by means of an adaptive threshold, the image is tranformed in a binary one;
*by means of an adaptive threshold, the image is tranformed in a binary one;
*the distance between every point and the nearest black pixel is calculated;
*the distance between every point and the nearest black pixel is calculated;
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*on this last image the [http://en.wikipedia.org/wiki/Watershed_%28algorithm%29 watershed] algorithm is applied: every pixel is assigned a label, depending on the segmentated region it belongs; then, every labeled region is represented with a different colour in the images as below.
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*on this last image the [http://en.wikipedia.org/wiki/Watershed_%28algorithm%29 watershed] algorithm is applied: every pixel is assigned a label, depending on the segmentated region it belongs; then, every labeled region is represented with a different colour, as in the images shown below.

Revision as of 13:55, 19 October 2007

Using the experimental set up illustrated here, we obtain short film segments of our fluorescent bacteria; then, with an apposite software, we extract several frames from each of them. Examples of images we get are shown below.


Orig1.jpg
Orig22.jpg


Since we need to know how much of the total image area is occupied by bacteria, we process the images with our segmentation algorithm implemented in Matlab.

Here are the processing phases:

  • the RGB image is read and the green channel is extracted;
  • a morphological top hat filtering is performed on the grayscale image;
  • by means of an adaptive threshold, the image is tranformed in a binary one;
  • the distance between every point and the nearest black pixel is calculated;
  • on this last image the watershed algorithm is applied: every pixel is assigned a label, depending on the segmentated region it belongs; then, every labeled region is represented with a different colour, as in the images shown below.


Segm1.jpg
Segm2.jpg







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