Gradient based intensity normalization.

Intensity normalization is important in quantitative image analysis, especially when extracting features based on intensity. In automated microscopy, particularly in large cellular screening experiments, each image contains objects of similar type (e.g. cells) but the object density (number and size of the objects) may vary markedly from image to image. Standard intensity normalization methods, such as matching the grey-value histogram of an image to a target histogram from, i.e. a reference image, only work well if both object type and object density are similar in the images to be matched. This is typically not the case in cellular screening and many other types of images where object type varies little from image to image, but object density may vary dramatically. In this paper, we propose an improved form of intensity normalization which uses grey-value as well as gradient information. This method is very robust to differences in object density. We compare and contrast our method with standard histogram normalization across a range of image types, and show that the modified procedure performs much better when object density varies between images.

Gradient watersheds in morphological scale-space.

After introducing several results relating to the modification of the homotopy of gradient functions based on extrema in the base image and building on earlier results in morphological scale-space, we introduce a scale-space monotonicity theorem for regions of an image defined by watersheds of a gradient function modified to retain only the local minima or maxima of its smoothed parent image. We then illustrate the theorem with an example of the scale-space extraction of texture features from the nuclei of cervical cells.

Dietetic staffing in Australian general hospitals.

In order to ascertain the existing and required staffing patterns of Australian dietitians, the 190 general hospitals in Australia which claim to provide an on premises dietetic service were surveyed. The questionnaire was directed to the dietitian in charge at each hospital. The 97 responses have been analysed to produce a histogram of hospital versus beds, histograms of hospitals versus required and existing dietitians, a correlation matrix for staffing predictors, regression analysis of staffing versus the predictors, a discussion of variance, and other results.