Scale-space and boundary detection in ultrasonic imaging using nonlinear signal-adaptive anisotropic diffusion

In this paper we develop a strategy for scale-space filtering and boundary detection in medical ultrasonic imaging. The strategy integrates a signal model for displayed ultrasonic images with the nonlinear anisotropic diffusion. The usefulness of the strategy is demonstrated for applications in volume rendering and automatic contour detection. The discrete implementation of anisotropic diffusion is based on a minimal nonlinear basis filter which is iterated on the input image. The filtering scheme involves selection of a threshold parameter which defines the overall noise level and the magnitude of gradients to be preserved. In displayed ultrasonic images the speckle noise is assumed to be signal dependent, and we have therefore developed a scheme which adaptively adjusts the threshold parameter as a function of the local signal level. The anisotropic diffusion process tends to produce artificially sharp edges and artificial boundary corners. Another modification has therefore been made to avoid edge-enhancement by leaving significant monotone sections unaltered. The proposed filtering strategy is evaluated both for synthetic images and real ultrasonic images.