The interpretation of geophysical data, such as images of subsurface rocks (seismic data, borehole scans), requires one in particular to perform an elaborate segmentation analysis on strongly textured, anisotropic, and not necessarily brightness-contrasted images. In this paper we explore the possibility of deriving new segmentation algorithms from recent advances in the neural modelling of pre-attentive segmentation in human vision. More specifically we consider a neural model proposed by Zhaoping Li. First, we reproduce some specific results obtained by Zhaoping Li on simple artificial and real images sharing some textural characteristics with geophysical data. Next, from the analysis of the model behaviour, we propose an image processing workflow depending on the textural characteristics and on the type of segmentation (contour enhancement or texture edge detection) one is interested in. With this algorithm one gets promising results: from the computation of a single attribute one extracts the oriented textured feature boundaries without prior classification.