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Image mining is the application of computer-based techniques that extract and exploit information from large image sets to support human users in generating knowledge from these sources. This review focuses on biomedical applications, in particular automated imaging at the cellular level. An image database is an interactive software application that combines data management, image analysis and visual data mining. The main characteristic of such a system is a layer that represents objects within an image, and that represents a large spectrum of quantitative and semantic object features. The image analysis needs to be adapted to each particular experiment, so ‘end-user programming’ will be desirable to make the technology more widely applicable. [Copyright &y& Elsevier]

Microscopic thin section images are a major source of information on physical properties, crystallization processes, and the evolution of rocks. Extracting the boundaries of grains is of special interest for estimating the volumetric structure of sandstone. To deal with large datasets and to relieve the geologist from a manual analysis of images, automated methods are needed for the segmentation task. This paper evaluates the region competition framework, which also includes region merging . The procedure minimizes an energy functional based on the Minimum Description Length (MDL) principle. To overcome some known drawbacks of current algorithms, we present an extension of MDL-based region merging by integrating edge information between adjacent regions. In addition, we introduce a modified implementation for region competition for overcoming computational complexities when dealing with multiple competing regions. Commonly used methods are based on solving differential equations for describing the movement of boundaries, whereas our approach implements a simple updating scheme. Furthermore, we propose intensity features for reducing the amount of data. They are derived by comparing theoretical values obtained from a model function describing the intensity inside uniaxial crystals with measured data. Error, standard deviation, and phase shift between the model and intensity measurements preserve sufficient information for a proper segmentation. Additionally, identified objects are classified into quartz grains, anhydrite, and reaction fringes by these features. This grouping is, in turn, used to improve the segmentation process further. We illustrate the benefits of this approach by four samples of microscopic thin sections and quantify them in a comparison of a segmentation result and a manually obtained one. [ABSTRACT FROM AUTHOR]

Abstract: Electrical borehole wall images represent micro-resistivity measurements at the borehole wall. The lithology reconstruction is often based on visual interpretation done by geologists. This analysis is very time-consuming and subjective. Different geologists may interpret the data differently. In this work, linear discriminant analysis (LDA) in combination with texture features is used for an automated lithology reconstruction of ODP (Ocean Drilling Program) borehole 1203A drilled during Leg 197. Six rock groups are identified by their textural properties in resistivity data obtained by a Formation MircoScanner (FMS). Although discriminant analysis can be used for multi-class classification, non-optimal decision criteria for certain groups could emerge. For this reason, we use a combination of 2-class (binary) classifiers to increase the overall classification accuracy. The generalization ability of the combined classifiers is evaluated and optimized on a testing dataset where a classification rate of more than 80% for each of the six rock groups is achieved. The combined, trained classifiers are then applied on the whole dataset obtaining a statistical reconstruction of the logged formation. Compared to a single multi-class classifier the combined binary classifiers show better classification results for certain rock groups and more stable results in larger intervals of equal rock type. [Copyright &y& Elsevier]