Thinning algorithms, such as the prairie fire or Medial Axis Transformation (MAT) algorithm, are used to extract structure-preserving networks or skeletons from segmented imagery. This is a useful function in image understanding applications where syntactical representation of object shapes is desired. The MAT has several shortcomings, however. The MAT skeletons thinned from two similar shapes may be structurally different due to the introduction of random noise into the segmentation process. This noise may exist as random "holes" within the segmented shape, as minor contour variations, or as spatial quantization effects. This problem is often solved by filtering the image prior to segmentation or thinning, but fine detail may be lost as a result. A syntactical method of removing these noise artifacts from image skeletons and of inferring a unique structure is demonstrated. The algorithms for performing this syntactical processing are coded in LISP. Conditions under which image processing functions are served best by the LISP environment are discussed. Image enhancement and noise are discussed in terms that embrace statistical and syntactical methods of image processing.
Thomas C. Rearick,
"Syntactical Methods For Improvement Of The Medial Axis Transformation", Proc. SPIE 0548, Applications of Artificial Intelligence II, (5 April 1985); doi: 10.1117/12.948412; https://doi.org/10.1117/12.948412