Detection and quantification of pore, solid and gravel spaces in CT images of a 3D soil sample

© 2020 Elsevier Inc. In this study, we scanned the core of a cylindrical soil sample (60mm diameter and 100mm height) by X-ray Computed Tomography (CT) producing 300 consecutive 2D digital images with 16-bit gray level depth and a resolution of 32 microns (image size 676 × 676 pixels). The aim of this work was to determine the geometry and spatial distribution of the elements in a sample, related in this case to pore, solid and gravel, inside each 2D image for the latter reconstruction of the corresponding 3D approximation of the elements using the total set of 300 soil images. Therefore, it was possible to determine the relative percentage of each element present in each 2D image and, correspondingly, the structure and total percentage in the 3D reconstruction. The identification of elements in the 2D image slices was very well accomplished using three standard segmentation algorithms: k-Means, Fuzzy c-Means and Otsu multilevel. In order to compare and evaluate the quality of results, a non-uniformity (NU) measure was applied such that low values were indicative of homogeneous regions. Due to the depth of the greyscale of the images, the results were very similar with comparable statistics and homogeneity (NU values) among the detected materials of the three algorithms. That suggests that the pore, solid and gravel spaces were very well identified, and this is reflected through their connectivity in the 3D reconstruction. Additionally, the gray level depth was reduced to 8 bits and the same study was undertaken. In this case, the quality of results was comparable to the previous ones, as the number of elements and NU values were very close. However, this also depends largely on the high resolution of the images. Thereby, the soil sample of this work was very well characterized using the simplest and most common algorithms for image segmentation thanks to the high contrast and resolution, and regardless the depth of the grey-level

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detectio

Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm

We take the concept of typicality from the field of cognitive psychology, and we apply the meaning to the interpretation of numerical data sets and color images through fuzzy clustering algorithms, particularly the GKPFCM, looking to get better information from the processed data. The Gustafson Kessel Possibilistic Fuzzy c-means (GKPFCM) is a hybrid algorithm that is based on a relative typicality (membership degree, Fuzzy c-means) and an absolute typicality (typicality value, Possibilistic c-means). Thus, using both typicalities makes it possible to learn and analyze data as well as to relate the results with the theory of prototypes. In order to demonstrate these results we use a synthetic data set and a digitized image of a glass, in a first example, and images from the Berkley database, in a second example. The results clearly demonstrate the advantages of the information obtained about numerical data sets, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used. This approach allows the identification of small homogeneous regions, which are difficult to find

Correlations between physical and chemical defences in plants: tradeoffs, syndromes, or just many different ways to skin a herbivorous cat?

� Most plant species have a range of traits that deter herbivores. However, understanding of how different defences are related to one another is surprisingly weak. Many authors argue that defence traits trade off against one another, while others argue that they form coordinated defence syndromes. � We collected a dataset of unprecedented taxonomic and geographic scope (261 species spanning 80 families, from 75 sites across the globe) to investigate relationships among four chemical and six physical defences. � Five of the 45 pairwise correlations between defence traits were significant and three of these were tradeoffs. The relationship between species’ overall chemical and physical defence levels was marginally nonsignificant (P = 0.08), and remained nonsignificant after accounting for phylogeny, growth form and abundance. Neither categorical principal component analysis (PCA) nor hierarchical cluster analysis supported the idea that species displayed defence syndromes. � Our results do not support arguments for tradeoffs or for coordinated defence syndromes. Rather, plants display a range of combinations of defence traits. We suggest this lack of consistent defence syndromes may be adaptive, resulting from selective pressure to deploy a different combination of defences to coexisting species