Thursday, June 20, 2013

Which structuring elements to detect branched points?

In a 2D skeleton obtained after thinning of a binary image, pixels having 3 or 4 (or more...) neighbors are branched-points. Hit-or-miss operator can be used to detect branched points (or junction points). Structuring elements, SE, can be 3x3 arrays representing the pixels configuration to be detected. Here different 3x3 structuring elements were explored on some simple test images with the hit-or-miss operator implemented in mahotas 1.0 .
  • There are two SE to detect pixels with 4 neighbors, called here X0 and X1.
  • There are two set of SE detecting pixels with at least 3 pixels, a Y-like configuration and a T-like configuration. Each of these configurations can be rotated by 45°, making 16 different SE.
18 SE were found , since don't care pixels were used a branched points can be detected by different SE, a pixel detected by X0 should be detected by T4 too. The 18 SE can be displayed as 3x3 image:
Black:background pixel (0), Blue: foreground pixel (1), Red:Don't care pixel (0 or 1)
Applied on a small skeleton-like test image (left), two branched-points  (junction) and four end-points are detected:
On a larger image, from the SIID images database:
Hit-or-miss transformation applied on the skeleton (up right), yields end-points (lower right). Since the same branched-points can be detected by different structuring elements, branched points appear of a variable color according to the detection occurence. Moreoever, branched-points can be connected yielding a branched domain of pixels.

ipython notebook

HMT-SE.ipynb

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