Estimators are derived of tissue proportions from X-ray computed tomography (CT) images. These take into account that many pixels in such images are responses to mixtures of tissue types. The problem is motivated by an application involving estimation of sheep tissue weights. The standard estimator, a count of the number of pixels in a particular range of values, is compared with the maximum likelihood fit of a mixed-pixel distribution and a moment-based estimator. Both simulations and the application show the moment estimator to be best.

We consider the use of principal component analysis to summarize the variation in labelled landmark data for images which are reversible in the sense that a mirror image may be defined for each image and the original and mirror images may be regarded as equally representative of the population. We examine the effect of including the original and mirror images on a principal component analysis based on the landmark co-ordinates. The inclusion of mirror images is found to lead to a simplified interpretation in which some components measure asymmetry in the images and the remainder depend symmetrically on pairs of co-ordinates. This is illustrated on shape variation in carrots. A second application is to the segmentation of X-ray computed tomography images of sheep to locate the inner boundary of the carcass. It is found that image boundaries can be identified more accurately by modelling them with principal components, and that including mirror images can offer a further improvement in accuracy. Similar arguments apply when a population of images is thought to be invariant under a rotation and may also be relevant when a principal component analysis is applied to descriptive statistics such as Fourier sums. Copyright 2004 Royal Statistical Society.

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Purpose: Accurate contour delineation is crucial for radiotherapy. Atlas based automatic segmentation tools can be used to increase the efficiency of contour accuracy evaluation. This study aims to optimize technical parameters utilized in the tool by exploring the impact of library size and atlas number on the accuracy of cardiac contour evaluation. Methods: Patient CT DICOMs from RTOG 0617 were used for this study. Five experienced physicians delineated the cardiac structures including pericardium, atria and ventricles following an atlas guideline. The consistency of cardiac structured delineation using the atlas guideline was verified by a study with four observers and seventeen patients. The CT and cardiac structure DICOM files were then used for the ABAS technique.To study the impact of library size (LS) and atlas number (AN) on automatic contour accuracy, automatic contours were generated with varied technique parameters for five randomly selected patients. Three LS (20, 60, and 100) were studied using commercially available software. The AN was four, recommended by the manufacturer. Using the manual contour as the gold standard, Dice Similarity Coefficient (DSC) was calculated between the manual and automatic contours. Five‐patient averaged DSCs were calculated for comparison for each cardiac structure.In order to study the impact of AN, the LS was set 100, and AN was tested from one to five. The five‐patient averaged DSCs were also calculated for each cardiac structure. Results: DSC values are highest when LS is 100 and AN is four. The DSC is 0.90±0.02 for pericardium, 0.75±0.06 for atria, and 0.86±0.02 for ventricles. Conclusion: By comparing DSC values, the combination AN=4 and LS=100 gives the best performance. This project was supported by NCI grants U24CA12014, U24CA180803, U10CA180868, U10CA180822, PA CURE grant and Bristol‐Myers Squibb and Eli Lilly.