Biomedical subjects

Alfredo Ruggeri

Publications and source records attributed to Alfredo Ruggeri.

3 recordsLinked to original sources

PubMed2005

Luminosity and contrast normalization in retinal images.

Retinal images are routinely acquired and assessed to provide diagnostic evidence for many important diseases, e.g. diabetes or hypertension. Because of the acquisition process, very often these images are non-uniformly illuminated and exhibit local luminosity and contrast variability. This problem may seriously affect the diagnostic process and its outcome, especially if an automatic computer-based procedure is used to derive diagnostic parameters. We propose here a new method to normalize luminosity and contrast in retinal images, both intra- and inter-image. The method is based on the estimation of the luminosity and contrast variability in the background part of the image and the subsequent compensation of this variability in the whole image. The application of this method on 33 fundus images showed an average 19% (max. 45%) reduction of luminosity variability and an average 34% (max. 85%) increment of image contrast, with a remarkable improvement, e.g., over low-pass correction. The proposed image normalization technique will definitely improve automatic fundus images analysis but will also be very useful to eye specialists in their visual examination of retinal images.

Fluorescein Angiography↗

PubMed2004

POPED, a software for optimal experiment design in population kinetics.

Population kinetic analysis is the methodology used to quantify inter-subject variability in kinetic studies. It entails the collection of (possibly sparse) data from dynamic experiments in a group of subjects and their quantitative interpretation by means of a mathematical model. This methodology is widely used in the pharmaceutical industry (where it is termed "pharmacokinetic population analysis") and recently it is becoming increasingly used in other areas of biomedical research. Unlike traditional kinetic studies, where the number of subjects can be quite small, population kinetic studies require large numbers of subjects. It is, therefore, of great interest to design these studies in the most efficient manner possible, to maximize the information content provided by the data. In this paper we propose an algorithm and a computer program, POPED, for the optimal design of a population kinetic experiment. In particular, the number of samples for each subject and the design of the individual sampling strategies, i.e. the number and location of the time points at which the output variable is sampled, will be considered. Among the various criteria proposed in the literature, D and ED optimality are the ones implemented in our software program, since they are the most widely used. A brief description of the techniques employed to perform design optimization is given, together with some details on their actual implementation. Some examples are then presented to show the program usage and the results provided.

Algorithms↗

PubMed2002

Automatic recognition of cell layers in corneal confocal microscopy images.

A confocal microscope can produce gray-scale images of the different layers of the cornea. We have addressed the problem of classifying these images, i.e. recognizing the layer displayed, using the shape of the cells contained, which is uniquely related to each specific layer. A first method was designed, based first on the binarization of the image and then on the description of the cell shape by means of Hu variables (central moments). An artificial neural network was used to classify each image according to the values assumed by these variables. A Matlab prototype of the classification system was developed, considering images of three corneal layers (Bowman membrane, stroma, endothelium) in normal subjects. The system was tested on 46 images, and good results were obtained. To avoid the critical step of binarization, an alternative cell shape description was investigated, based on Zernike moments, and a new network was developed and trained. The results achieved were better than those obtained with the previous technique, and also, no binarization was necessary.

Cell Size↗