ITK Pre-Processing Algorithm Wrappers in Python/GradientAnisotropicDiffusionFilter

Gradient Anisotropic Diffusion Filter

This filter is implemented using the itkGradientAnistropicDiffusionImageFilter class in ITK. It is one of the smoothing algorithms that preserves edges. Isotropic diffusion reduces the noise in an image; however, it also smooths the edges. Gradient anisotropic diffusion adjust the conductance parameter such that considerable diffusion is performed in the interior regions, where the gradient is small. At the edges, the gradient is larger and so the diffusion is lesser.

Parameters:

• 1. TimeStep - this parameter refers to the time step involved in solving the partial differential equation in the algorithm. For 2D, the typical value to use is 0.125. In general, for n-dimensional images, the timestep should be restricted to be less than ^[1].

• 2. NumberOfIterations - this specifies the number of iterations that the solver must perform before returning a solution image. More the number of iterations, more smooth the image would be. A typical number for the number of iteration is 5^[1].

• 3. Conductance - this parameter controls conductance, which in turn controls the sensitivity of the algorithm in preserving the edges. If the value of the conductance is low, the algorithm preserves the image features to a larger extent. If the value of the conductance is high, the algorithm smooths (or diffuses) the features in the image. Typical values range from 0.5 to 2.0, but in general it will depend on the type of image and the number of iterations^[1].

Figures 1 and 2 show how the increasing the number of iterations increasingly smooths the image seg_orig. As the number of iterations increases, the edges also start smoothing out.

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Fig. 1: Gradient aniosotropic diffusion applied to seg_orig, with conductance = 2, and num. of iterations = 5.

Fig. 2: Gradient aniosotropic diffusion applied to seg_orig, with conductance = 2, and num. of iterations = 10.