Carbonate rocks are among the sedimentary systems which preserve information on the formation and diagenetic history expressed in its composition (distribution of its major rock-forming components (RFC). For estimating RFC proportions at the micro-scale, a simple counting of visible RFCs in thin sections using overlaid grids is a long-used, well-established technique. However, computer tomography (CT) analysis provides us with quantitative data in 3D at both the scale of the entire sample and a resolution defined by dimensions of the voxels at the micro-scale. The quantitative data expressed in Hounsfield units (HU) correlates with the density of RFCs. In this work statistical properties of CT-based data for selected freshwater carbonate samples from the Danube-Tisza Interfluve have been assessed using histograms and boxplots. Univariate statistical parameters characterize each sample. The maximum-likelihood method of mixture analysis has been adapted to recover and estimate the parameters of these subpopulations. Subpopulations have been defined in the form of overlapping intervals using statistical parameters gained (mean±2STD). Five major components have been defined: empty and partially or entirely filled pores by calcite, limestone micrite, dolomite micrite matrix and limonite saturated matrix.

With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures (e.g. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological surgeons. In this paper, we present one of the most important steps towards building such a system: a new approach to kidney segmentation from Computed Tomography data. The segmentation is based on the Active Contour Method using the Level Set (LS) framework. During the segmentation process the energy functional describing an image is the subject to minimize. The functional proposed in this paper consists of four terms. In contrast to the original approach containing solely the region and boundary terms, the ellipsoidal shape constraint was also introduced. This additional limitation imposed on evolution of the function prevents from leakage to undesired regions. The proposed methodology was tested on 10 Computed Tomography scans from patients diagnosed with renal cancer. The database contained the results of studies performed in several medical centers and on different devices. The average effectiveness of the proposed solution regarding the Dice Coefficient and average Hausdorff distance was equal to 0.862 and 2.37 mm, respectively. Both the qualitative and quantitative evaluations confirm effectiveness of the proposed solution.