Hierarchical Figure-Based Shape Description for Medical Imaging

Abstract

Medical imaging has long needed a good method of shape description, both to quantitate shape and as a step toward object recognition. Despite this need none of the shape description methods to date have been sufficiently general, natural, and noise-insensitive to be useful. We have developed a method that is automatic and appears to have great hope in describing the shape of biological objects in both 0 and 3D.

The method produces a shape description in the form of a hierarchy by scale of simple symmetric axis segments. An axis segment that is a child of another has smaller scale and is seen as a branch of its parent. The scale value and parent-child relationship are induced by following the symmetric axis under successive reduction of resolution. The result is a figure-rather than boundary-oriented shape description that has natural segments and is insensitive to noise in the object description.

We extend this method to the description of grey-scale images. Thus, model-directed pattern recognition will not require pre-segmentation followed by shape matching but rather will allow shape properties to be included in the segmentation itself.

The approach on which this method is based is generally applicable to producing hierarchies by scale. It involves following a relevant feature to annihilation as resolution is reduced, defining the component that is annihilating as a basic subobject, and letting the component into which annihilation takes place become its parent in the hierarchy.