A dynamical model with adaptive pixel moving for microarray images segmentation

Gene expression analysis is one of the main research issues in computational biology. Such analysis can provide very relevant information related to cell activity. Several techniques have been employed for this analysis. One of them is the analysis of microarray images. This paper proposes a new data clustering method based on dynamical system modelling for the segmentation of microarray images.

The proposed approach employs a network consisting of interacting elements, where each element represents an input data as an attribute vector. Each element of the network receives attractions from other elements within a certain region. Those attractions, determined by a predefined similarity measure, drive the elements to converge to their corresponding cluster centre. With this model, neither the number of pixel clusters nor the initial guessing of cluster centres is required. Moreover, the proposed model allows the omission of the gridding process. The results obtained so far have been very promising.