Abstract
This paper describes a segmentation method for automating the region of interest (ROI) delineation in chromatographic images, thus allowing the definition of the image area that contains the fundamental information for further processing while excluding the frame of the chromatographic plate that does not contain relevant data for disease identification. This is the first component of a screening tool for Fabry disease, which will be based on the automatic analysis of the chromatographic patterns extracted from the image ROI. Image segmentation is performed in two phases, where each individual pixel is finally considered as frame or ROI. In the first phase, an unsupervised learning method is used for classifying image pixels into three classes: frame, ROI or unknown. In the second phase, distance features are used for deciding which class the unknown pixels belong to. The segmentation result is post-processed using a sequence of morphological operators in order to obtain the final ROI rectangular area. The proposed methodology was successfully evaluated in a dataset of 41 chromatographic images.

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In this paper, we examine the possibility that the spatiotemporal receptive field properties of visual cortical neurons can be understood in terms of a statistically efficient strategy for encoding natural time-varying images. It is believed that the sense of object motion and velocity are also related to these fields, as objects in natural scenes are represented by a sparse set of statistically independent components, such as edges. Currently, computational models of receptive fields consider only spatial components, and thus cannot account for time-varying sensory stimuli In this paper, a model based on independent components analysis and cellular neural networks is proposed. We describe an artificial neural network that attempts to accurately reconstruct its spatiotemporal input data while simultaneously reducing the statistical dependencies between its outputs, as advocated by the redundancy reduction principle. This approach extends existing models to incorporate temporal aspects of sequences of images of natural scenes.

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