The goal of image interpretation is to convert raw image data into me-
ingful information. Images are often interpreted manually. In medicine,
for example, a radiologist looks at a medical image, interprets it, and
tra- lates the data into a clinically useful form. Manual image
interpretation is, however, a time-consuming, error-prone, and
subjective process that often requires specialist knowledge. Automated
methods that promise fast and - jective image interpretation have
therefore stirred up much interest and have become a signi?cant area of
research activity. Early work on automated interpretation used low-level
operations such as edge detection and region growing to label objects in
images. These can p- ducereasonableresultsonsimpleimages,
butthepresenceofnoise, occlusion, andstructuralcomplexity
oftenleadstoerroneouslabelling. Furthermore, - belling an object is
often only the ?rst step of the interpretation process. In order to
perform higher-level analysis, a priori information must be incor- rated
into the interpretation process. A convenient way of achieving this is
to use a ?exible model to encode information such as the expected size,
shape, appearance, and position of objects in an image. The use of
?exible models was popularized by the active contour model, or 'snake'
[98]. A snake deforms so as to match image evidence (e.g., edges)
whilst ensuring that it satis?es structural constraints. However, a
snake lacks speci?city as it has little knowledge of the domain,
limiting its value in image interpretation.
