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A procedure is developed to quantify and improve the signal-to-noise ratio (SNR) of magnetic resonance images. The image SNR is quantified using the correlation function of two independent acquisitions of an image. To test the performance of the quantification, SNR measurement data are fitted to theoretically expected curves. The proposed correlation(More)
This paper investigates a new approach to data clustering. The probability density function (p.d.f.) is estimated by using the Parzen window technique. The p.d.f, thresholding permits the segmentation of the data space by influence zones (SKIZ algorithm). A bottom-up thresholding procedure is iterated to refine the segmentation. As a result, a complete(More)
The present review tries to identify some trends among the multitude of ways followed by image processing developments in the field of microscopy. Nine topics were selected. They cover the fields of: signal processing, statistical analysis, artificial intelligence, three-dimensional microscopy, multidimensional microscopy, multimodality microscopy, theory,(More)
Although most histogram-based image segmentation methods rely on the identiÿcation of a good threshold, we show that thresholding is not mandatory. Instead, we propose the association of grades of membership to each individual pixel, in order to perform probabilistic relaxation in the image space (which realizes some kind of regularization) and ÿnally to(More)
Whereas estimating the number of clusters is directly involved in the ®rst steps of unsupervised classi®cation procedures , the problem still remains topical. In our attempt to propose a solution, we focalize on procedures that do not make any assumptions on the cluster shapes. Indeed the classi®cation approach we use is based on the estimation of the(More)
At the present time, one of the best methods for semi-automatic image segmentation seems to be the approach based on the fuzzy connectedness principle. First, we identify some deficiencies of this approach and propose a way to improve it, through the introduction of competitive learning. Second, we propose a different approach, based on watersheds. We show(More)
We investigated whether cluster formation by noninvasive cells can be explained by a global attractive potential. Indices quantifying the persistence of migration in experimental conditions were compared to the same indexes computed from simulations with a density-based cellular automaton. The results indicate that the attractive potential hypothesis must(More)