background: the artificial neural networks (anns) are useful in solving nonlinear processes, without the need for mathematical models of the parameters. since the relationship between the ct numbers and material compositions is not linear, ann can be used for obtaining tissue density and composition. objective: the aim of this study is to utilize ann for determination of the composition and mas...

Introduction: In radiotherapy treatment planning system (TPS), basic input is the data from computed tomography (CT) scan, which takes into account the effect of inhomogeneities in dose calculations. Measurement of CT numbers may be affected by scanner-specific parameters. Therefore, it is important to verify the effect of different CT scanning protocols on Hounsfield unit (HU) and its impact o...

A corn density calibration was developed for a near-infrared transmittance instrument (Infratec 1225). The calibration sample set included 96 corn samples grown in various locations of the United States. Samples were selected by principal component analysis (PCA) from a larger set of 410 samples, representing the 1986 through 1992 crop years. Samples for instrument and temperature stabilization...

BACKGROUND Breast density is a significant breast cancer risk factor measured from mammograms. The most appropriate method for measuring breast density for risk applications is still under investigation. Calibration standardizes mammograms to account for acquisition technique differences prior to making breast density measurements. We evaluated whether a calibration methodology developed for an...

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010) and Gneiting, T. and Ranjan, R. (2013), we use in finite beta mixtures for the calibration. The proposed...

An efficient technique for calibrating video cameras to record optical density (OD) from microscopic images is described. The method corrects for variation over the field of the brightfield and darkfield intensities, does not assume a linear response of the camera to the incident intensity and requires a single calibration filter.

Soybean physical and chemical properties changed by size (from 4.8 to 8.8 mm diameter), but soybean size and seed density did not affect the protein and oil determination accuracy of three near-infrared transmission analyzers. Corn samples were also separated by size and kernel density. Changes in corn kernel density and size introduced small errors in near-infrared transmission protein, oil, a...