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. Furthermore, to classify an unknown sample using image analysis, a classification rule must be designed and evaluated. Unfortunately, statistical evaluation methods used in the literature of nuclear image analysis are often inappropriate. The present article discusses some of the difficulties in statistical evaluation of nuclear image analysis, and a study of cervical cancer is presented in order to illustrate the problems. In conclusion, some of the most severe errors in nuclear image analysis occur in analysis of a large feature set, including few patients, without confirming the results on an independent data set. To select features, Bonferroni correction for multiple test is recommended, together with a standard feature set selection method. Furthermore, we consider that the minimum requirement of performing statistical evaluation in nuclear image analysis is confirmation of the results on an independent data set. We suggest that a consensus of how to perform evaluation of diagnostic and prognostic features is necessary, in order to develop reliable tools for clinical use, based on nuclear image analysis.

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A novel system for on-line measurement of fat content in inhomogeneous pork trimmings is presented. The system allows near infrared (NIR) energy to interact with the meat using non-contact optics while it is travelling in large plastic boxes on a conveyor belt. A comparison was made between the log of the inverse of the interactance NIR spectra [log(1/T)], standard normal variate (SNV) and extended multiplicative signal correction (EMSC) as techniques for the correction of physical light scattering due to colour and textural differences, height variation and temperature fluctuations, depending on whether the meat was warm-cut or cold-cut. EMSC gave the best prediction results; a robust partial least squares regression using two factors resulted in a root mean square error (RMSEP) of 1.9% on 20 kg batches of inhomogeneous meat trimmings. The model was fully tested twice in an on-line environment at a slaughter house and performed with a RMSEP of 3.4% for a fat range of 8-55% in the first industrial trial and 2.82% in the second industrial trial.