Texture and spatial pattern are important attributes of images and their potential as features in image classification, for
example to discriminate between normal and abnormal status in medical images, has long been recognized. In order to
be clinically useful, a texture metric should be robust to changes in image acquisition and digitization. We compared
four multi-scale texture metrics accessible in the spatial domain (lacunarity, average local variance (ALV), and two
novel variations) in terms of ease of interpretation, sensitivity and computational cost. We analyzed a variety of patterns
and textures, using simple synthetic images, standard texture images, and three-dimensional point distributions. ALV is
invariant to brightness, but depends on image contrast; it detects the size of a pattern element as a large peak in the plot.
Lacunarity shows the periodicity within an image. Normalizing lacunarity removes its dependence on image density, but
not on image brightness and contrast, so that comparisons should always be made using histogram equalized images. We
extended the treatment to grayscale images directly, which is not equivalent to a weighted sum of the normalized
lacunarity of the bit-plane images. Different sampling schemes were introduced and compared in terms of resolution and
computational tractability. The plots can be used directly as a texture signature, and parametric features can be extracted
from monotonic lacunarity plots for classification purposes.