Ridge and valley enhancing filters are widely used in applications such as vessel detection in medical image computing. When images are degraded by noise or include vessels at different scales, such filters are an essential step for meaningful and stable vessel localization. In this work, we propose a novel multi-scale anisotropic fourth-order diffusion equation that allows us to smooth along vessels, while sharpening them in the orthogonal direction. The proposed filter uses a fourth-order diffusion tensor whose eigentensors and eigenvalues are determined from the local Hessian matrix, at a scale that is automatically selected for each pixel. We discuss efficient implementation using a fast explicit diffusion scheme and demonstrate results on synthetic images and vessels in fundus images. Compared to previous isotropic and anisotropic fourth-order filters, as well as established second-order vessel enhancing filters, our newly proposed one better restores the centerlines in all cases.
@cite_7: We introduce a new method for image smoothing based on a fourth-order PDE model. The method is tested on a broad range of real medical magnetic resonance images, both in space and time, as well as on nonmedical synthesized test images. Our algorithm demonstrates good noise suppression without destruction of important anatomical or functional detail, even at poor signal-to-noise ratio. We have also compared our method with related PDE models.
@cite_1: 
@cite_32: Regularization with functions of bounded variation has been proven to be effective for denoising signals and images. This nonlinear regularization technique, in contrast with linear regularization techniques like Tikhonov regularization, has the advantage that discontinuities in signals and images can be located very precisely. In this paper bounded variation regularization is generalized to functions with higher order derivatives of bounded variation. This concept is applied to locate discontinuities in derivatives, which has important applications in parameter estimation problems.
@cite_12: A class of fourth-order partial differential equations (PDEs) are proposed to optimize the trade-off between noise removal and edge preservation. The time evolution of these PDEs seeks to minimize a cost functional which is an increasing function of the absolute value of the Laplacian of the image intensity function. Since the Laplacian of an image at a pixel is zero if the image is planar in its neighborhood, these PDEs attempt to remove noise and preserve edges by approximating an observed image with a piecewise planar image. Piecewise planar images look more natural than step images which anisotropic diffusion (second order PDEs) uses to approximate an observed image. So the proposed PDEs are able to avoid the blocky effects widely seen in images processed by anisotropic diffusion, while achieving the degree of noise removal and edge preservation comparable to anisotropic diffusion. Although both approaches seem to be comparable in removing speckles in the observed images, speckles are more visible in images processed by the proposed PDEs, because piecewise planar images are less likely to mask speckles than step images and anisotropic diffusion tends to generate multiple false edges. Speckles can be easily removed by simple algorithms such as the one presented in this paper.
In a one-dimensional setting, discrete variants of higher order data regularization can be traced back to a 1922 article by Whittaker @cite_1 . A first approach for higher order regularization in image processing involving the absolute value of all second order partial derivatives has been proposed by Scherzer @cite_32 . The resulting method has the drawback of not being rotationally invariant. An extension of classical regularization by choosing @math as argument of the penalizer in the smoothness term has been proposed by You and Kaveh @cite_12 . Their method introduces speckle artifacts around edges that require some post-processing. Both problems can be solved by using the squared Frobenius norm of the Hessian matrix @math as argument of the penalizer. This has been proposed by @cite_7 .