3D edge detection by separable recursive filtering and edge closing

Abstract

Edge detection in 3D images such as scanner, magnetic resonance, or spatiotemporal data is considered. A two-stage scheme based on separable recursive filtering and edge tracking/closing is proposed. The key point of the filtering stage is to use optimal recursive and separable filters to approximate gradient or Laplacian methods. The recursive nature of the operators enables one to implement infinite 3D impulse response with a computing time roughly similar to a 3*3*3 convolution mask. The principle of the edge tracking/closing is to select from the previous stage only the more reliable edge points and then to apply an edge closing method derived from the idea developed by R. Deriche and J.P. Cocquerez (1988). This makes it possible to substantially improve the results provided by the filtering stage.<>