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Morphological skeleton

In digital image processing, morphological skeleton is a skeleton representation of a shape or binary image, computed by means of morphological operators.

Skeleton by openings
Lantuéjoul's formula Continuous images In (Lantuéjoul 1977), Lantuéjoul derived the following morphological formula for the skeleton of a continuous binary image X\subset \mathbb{R}^2: :S(X)=\bigcup_{\rho >0}\bigcap_{\mu >0}\left[(X\ominus \rho B)-(X\ominus \rho B)\circ \mu \overline B\right], where \ominus and \circ are the morphological erosion and opening, respectively, \rho B is an open ball of radius \rho, and \overline B is the closure of B. Discrete images Let \{nB\}, n=0,1,\ldots, be a family of shapes, where B is a structuring element, :nB=\underbrace{B\oplus\cdots\oplus B}_{n\mbox{ times}}, and :0B=\{o\}, where o denotes the origin. The variable n is called the size of the structuring element. Lantuéjoul's formula has been discretized as follows. For a discrete binary image X\subset \mathbb{Z}^2, the skeleton S(X) is the union of the skeleton subsets \{S_n(X)\}, n=0,1,\ldots,N, where: :S_n(X)=(X\ominus nB)-(X\ominus nB)\circ B. Reconstruction from the skeleton The original shape X can be reconstructed from the set of skeleton subsets \{S_n(X)\} as follows: :X=\bigcup_n (S_n(X)\oplus nB). Partial reconstructions can also be performed, leading to opened versions of the original shape: :\bigcup_{n\geq m} (S_n(X)\oplus nB)=X\circ mB. The skeleton as the centers of the maximal disks Let nB_z be the translated version of nB to the point z, that is, nB_z=\{x\in E| x-z\in nB\}. A shape nB_z centered at z is called a maximal disk in a set A when: • nB_z\in A, and • if, for some integer m and some point y, nB_z\subseteq mB_y, then mB_y\not\subseteq A. Each skeleton subset S_n(X) consists of the centers of all maximal disks of size n. == Performing Morphological Skeletonization on Images ==
Performing Morphological Skeletonization on Images
Morphological Skeletonization can be considered as a controlled erosion process. This involves shrinking the image until the area of interest is 1 pixel wide. This can allow quick and accurate image processing on an otherwise large and memory intensive operation. A great example of using skeletonization on an image is processing fingerprints. This can be quickly accomplished using bwmorph; a built-in Matlab function which will implement the Skeletonization Morphology technique to the image. The image to the right shows the extent of what skeleton morphology can accomplish. Given a partial image, it is possible to extract a much fuller picture. Properly pre-processing the image with a simple Auto Threshold grayscale to binary converter will give the skeletonization function an easier time thinning. The higher contrast ratio will allow the lines to joined in a more accurate manner. Allowing to properly reconstruct the fingerprint. skelIm = bwmorph(orIm,'skel',Inf); %Function used to generate Skeletonization Images ==Notes==
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