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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.
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