Ridge detection using Savitzky-Golay filtering and steerable second-order Gaussian derivatives

We propose a method for ridge detection at different widths using second-order Gaussian derivative masks. The width of the ridge extracted varies depending on the mask size and its parameter, σ. In the proposed method, the ridge orientations are estimated as an initial step by finding the zerocrossings of the first derivative of the second directional derivative. In order to compute the orientations from discrete samples of the image, we make use of the recently popularized Savitzky-Golay (S-G) filter. Once the directions are estimated, ridge detection is accomplished by steering a second-order Gaussian kernel, which closely approximates the ideal ridge template, in the computed directions. The method is computationally effective on two accounts: (1) The ridge orientations are determined efficiently using S-G filtering; and (2) Once the orientations are estimated, the steerability property is used to detect ridges. The output of the ridge detector is then improved using non-maximal suppression and hysteresis thresholding. The results obtained are compared with an efficient benchmark method for ridge extraction.