Individual and population age structures constitute essential knowledge for proper management of commercial fisheries. Despite the important advances made in age determination using otolith growth structures, there is still a need to improve both precision and accuracy. The problem of increasing precision in age estimations has been addressed via increasing automation in the identification of growth marks. However, approaches based on otolith size, weight, perimeter, and related measurements (including contour analysis) have moderate success in age prediction. Likewise, early attempts of image analysis have reported poor results, both in cases of 1D (grey-intensity profiles) or 2D images. Recent developments in image analysis have broken this trend, and fully automatic techniques could be an alternative for routine ageing in the near future. Here, we propose a new method for 2D feature extraction that provides robust numerical descriptors of the growth structures of otoliths.