Preliminary images from an adaptive imaging system.

I-ImaS (Intelligent Imaging Sensors) is a European project aiming to produce real-time adaptive X-ray imaging systems using Monolithic Active Pixel Sensors (MAPS) to create images with maximum diagnostic information within given dose constraints. Initial systems concentrate on mammography and cephalography. In our system, the exposure in each image region is optimised and the beam intensity is a function of tissue thickness and attenuation, and also of local physical and statistical parameters in the image.

Many are called, but few are chosen. Feature selection and error estimation in high dimensional spaces.

We address the problems of feature selection and error estimation when the number of possible feature candidates is large and the number of training samples is limited. A Monte Carlo study has been performed to illustrate the problems when using stepwise feature selection and discriminant analysis. The simulations demonstrate that in order to find the correct features, the necessary ratio of number of training samples to feature candidates is not a constant.

A review of caveats in statistical nuclear image analysis.

A large body of the published literature in nuclear image analysis do not evaluate their findings on an independent data set. Hence, if several features are evaluated on a limited data set over-optimistic results are easily achieved. In order to find features that separate different outcome classes of interest, statistical evaluation of the nuclear features must be performed.

Ultrastructural texture analysis as a diagnostic tool in mouse liver carcinogenesis.

Nuclear texture, which reflects the overall structure of the chromatin, may be used to detect early as well as later stages of malignancy. In this study, texture analysis was applied to four groups of liver cells in mice: normal and regenerating liver, hyperplastic nodules, and hepatocellular carcinomas. The best discriminating set of features was selected based on a training data set.