We present an entropic edge-detection method based on the Jensen-Shannon divergence, applied to grey level histograms obtained by sliding a window over an image. For every pixel in the image, we calculate the elements of a divergence matrix and a direction matrix, via spline approximation. Based on these estimated matrices, a new technique for thinning and linking unconnected edge pixels is also described. The global method is found to be an excellent technique for image segmentation. For example, it is very robust against noise, and it is specially appropriate in the interfacial tension measures obtained by means of contour images of liquid drops. Results show that the global method described give a better performance than other existing methods used in this field.

MOTIVATION: DNA sequences are formed by patches or domains of different nucleotide composition. In a few simple sequences, domains can simply be identified by eye; however, most DNA sequences show a complex compositional heterogeneity (fractal structure), which cannot be properly detected by current methods. Recently, a computationally efficient segmentation method to analyse such nonstationary sequence structures, based on the Jensen-Shannon entropic divergence, has been described. Specific algorithms implementing this method are now needed. RESULTS: Here we describe a heuristic segmentation algorithm for DNA sequences, which was implemented on a Windows program (SEGMENT). The program divides a DNA sequence into compositionally homogeneous domains by iterating a local optimization procedure at a given statistical significance. Once a sequence is partitioned into domains, a global measure of sequence compositional complexity (SCC), accounting for both the sizes and compositional biases of all the domains in the sequence, is derived. SEGMENT computes SCC as a function of the significance level, which provides a multiscale view of sequence complexity.