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.
We present two novel metrics for assessing scoliosis, in which the geometric centers of all the affected vertebrae in an
antero-posterior (A-P) radiographic image are used. This is in contradistinction to the existing methods of using selected
vertebrae, and determining either their endplates or the intersections of their diagonals, to define a scoliotic angle. Our
first metric delivers a scoliotic angle, comparable to the Cobb and Ferguson angles. It measures the sum of the angles
between the centers of the affected vertebrae, and avoids the need for an observer to decide on the extent of component
curvatures. Our second metric calculates the normalized root-mean-square curvature of the smoothest path comprising
piece-wise polynomial splines fitted to the geometric centers of the vertebrae. The smoothest path is useful in modeling
the spinal curvature. Our metrics were compared to existing methods using radiographs from a group of twenty subjects
with spinal curvatures of varying severity. Their values were strongly correlated with those of the scoliotic angles (r =
0.850 - 0.886), indicating that they are valid surrogates for measuring the severity of scoliosis. Our direct use of
positional data removes the vagaries of determining variably shaped endplates, and circumvented the significant interand
intra-observer errors of the Cobb and Ferguson methods. Although we applied our metrics to two-dimensional (2-
D) data in this paper, they are equally applicable to three-dimensional (3-D) data. We anticipate that they will prove to
be the basis for a reliable 3-D measurement and classification system.
The measurement of abnormal vascular tortuosity is important in the diagnosis of many diseases. Metrics based on three-dimensional (3-D) curvature, using approximate polynomial spline-fitting to "data balls" centered along the mid-line of the vessel, minimize digitization errors and give tortuosity values largely independent of the resolution of the imaging system. In order to establish their clinical validity we applied them to a number of clinical vascular systems, using both 2-D (standard angiograms and retinal images) and 3-D datasets (from computed tomography angiography (CTA) and magnetic resonance angiography (MRA)). Using the abdominal aortograms we found that the metrics correlated well with the ranking of an expert panel of three vascular surgeons. Both the mean curvature and the root-mean square curvature provided good discrimination between vessels of different tortuosity: and using a data ball size of one-quarter of the local vessel radius in the spline fitting gave consistent results. Tortuous retinal vessels resulting from retinitis or diabetes, but not from vasculitis, could be distinguished from normal vessels. Tortuosity values based on 3-D data sets gave higher values than their 2-D projections, and could easily be implemented in automatic measurement. They produced values sufficiently discriminating to assess the relative utility of arteries for endoluminal repair of aneurysms.
A convenient and systematic protocol for evaluating the image quality of digitized mammographic phantom images has been developed. It involves the measurement of the contrast of a low-contrast nodule and/or a group of microcalcifications from images of the American College of Radiology mammographic accreditation phantoms acquired under different x-ray techniques.