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Automated processing of digitized soilsection images reveals elements of soil structure and draws primary estimates of bioecological importance, like ground fertility and changes in terrestrial ecosystems. We examine a sophisticated integration of some modern methods from computer vision for image feature extraction, texture analysis, and segmentation into homogeneous regions, relevant to soil micromorphology. First, we propose the use of a morphological partial differential equation-based segmentation scheme based on seeded region-growing and level curve evolution with speed depending on image contrast. Second, we analyze surface texture information by modeling image variations as local modulation components and using multifrequency filtering and instantaneous nonlinear energy-tracking operators to estimate spatial modulation energy. By separately exploiting contrast and texture information, through multiscale image smoothing, we propose a joint image segmentation method for further interpretation of soil images and feature measurements. Our experimental results in images digitized under different specifications and scales demonstrate the efficacy of our proposed computational methods for soil structure analysis. We also briefly demonstrate their applicability to remote sensing images.