Advances in X-ray microtomography (XMT) are opening new opportunities for examining soil structural properties and fluid distribution around living roots in-situ. The low contrast between moist soil, root and air-filled pores in XMT images presents a problem with respect to image segmentation. In this paper, we develop an unsupervised method for segmenting XMT images to pores (air and water), soil, and root regions. A feature-based segmentation method is provided to isolate regions that consist of similar texture patterns from an image based on the normalized inverse difference moment of gray-level co-occurrence matrix. The results obtained show that the combination of features, clustering, and post-processing techniques has advantageous over other advanced segmentation methods.
In this paper, we propose a context adaptive nonlinear diffusion method for image denoising in wavelet domain which we call context based diffusion in stationary wavelet domain (SWCD). In diffusing detail coefficients, the method adapts to the local context such that strong edges are preserved and smooth regions are diffused in a greater extent. The local context which is derived directly from the transform energies at scales 1 and 2 of two-level stationary wavelet transform (SWT) controls the diffusion. The shift invariance of SWT contributes to the performance of the method. The experiment is conducted on a number of benchmark images and compared to recently developed denoising methods which explore the adaptation concept for wavelet shrinkage and diffusion. A comparison is performed also to a method of diffusing both approximation and detail coefficients. The proposed SWCD method outperforms recently proposed adaptive shrinkage and adaptive diffusion, particularly at high noise levels. The method is computationally efficient due to the Haar wavelet and fast convergence attained due to exploiting the context information.
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