Statistical Models of Shape

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.