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In this paper, we propose a new methodology for analysis of microarray images. First, a new gridding algorithm is proposed for determining the individual spots and their borders. Then, a Gaussian mixture model (GMM) approach is presented for the analysis of the individual spot images. The main advantages of the proposed methodology are modeling flexibility and adaptability to the data, which are well-known strengths of GMM. The maximum likelihood and maximum a posteriori approaches are used to estimate the GMM parameters via the expectation maximization algorithm. The proposed approach has the ability to detect and compensate for artifacts that might occur in microarray images. This is accomplished by a model-based criterion that selects the number of the mixture components. We present numerical experiments with artificial and real data where we compare the proposed approach with previous ones and existing software tools for microarray image analysis and demonstrate its advantages. Index Terms--Cross-validated likelihood, DNA microarray image analysis, expectation-maximization algorithm, Gaussian mixture models, Markov random fields, maximum a posteriori, maximum likelihood, microarray gridding.

Gaussian mixture models (GMMs) constitute a well-known type of prubabilistic neural networks. One of their many successful applications is in image segmentation, where spatially constrained mixture models have been trained using the expectation-maximization (EM) framework. In this letter, we elaborate on this method and propose a new methodology for the M-step of the EM algorithm that is based on a novel constrained optimization formulation. Numerical experiments using simulated images illustrate the superior performance of our method in terms of the attained maximum value of the objective function and segmentation accuracy compared to previous implementations of this approach. Index Terms--Covex quadratic programming (QP), expectation-maximization (EM), Gaussian mixture model (GMM), image segmentation, Markov random field (MRF).

In this article, we introduce Newtonian spectral clustering, a method that employs Newtonian preprocessing to promote cluster perspicuity and trajectory analysis to gain valuable affinity information. A simple two-body potential is used to model the interaction under the influence of which the points move according to Newton's second law. This procedure produces a transformed data set with reduced cluster overlap, which favors the spectral clustering approach. This is so, because the affinity matrix can be enriched with information derived from the underlying interaction model. Special care is also given to estimate the Gaussian kernel parameter, since its role is important for the clustering procedure. The method is further extended appropriately to treat problems of high dimensionality. We have tested the proposed methodology on several benchmark data and compared its performance to that of rival techniques. The superiority of the new approach is readily deduced by inspecting the reported results. [ABSTRACT FROM AUTHOR]

The use of global optimization (GO) techniques in few-body systems is discussed. Their usefulness is demonstrated by applying them to solve the two-body bound state problem by casting the wave function as an artificial neural network (ANN) as well as by employing them to locate zeros of the Jost function in the complex k-plane corresponding to bound and resonance states. [Copyright &y& Elsevier]