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Statistical Models of Appearance

Statistical models of shape and appearance (combined appearance models) were introduced by Cootes, Edwards, Lanitis and Taylor [2,3,7], and have since been applied extensively in medical image analysis [9,12,16]. The construction of an appearance model depends on establishing a dense correspondence across a training set of images using a set of landmark points marked consistently on each training image.

Fig. 1. The effect of varying the first model parameter of a brain appearance model by $\pm2.5$ standard deviations.

Using the notation of Cootes [3], the shape (configuration of landmark points) can be represented as a vector $\mathbf{x}$ and the texture (intensity values) represented as a vector $\mathbf{g}$.

The shape and texture are controlled by statistical models of the form

$$\begin{array}{cc} \mathbf{x}=\mathbf{\overline{x}}+\mathbf{P... ...}=\overline{\mathbf{g}}+\mathbf{P}_{g}\mathbf{b}_{g}\end{array}$$ (3)

Where $\mathbf{b}_{s}$ are shape parameters, $\mathbf{b}_{g}$ are texture parameters, $\mathbf{\overline{x}}$ and $\overline{\mathbf{g}}$ are the mean shape and texture, and $\mathbf{P}_{s}$ and $\mathbf{P}_{g}$ are the principal modes of shape and texture variation respectively.

Since shape and texture are often correlated, we can take this into account in a combined statistical model of the form

$$\begin{array}{cc} \mathbf{x}=\bar{\mathbf{x}}+\mathbf{Q}_{s}... ...\mathbf{g}=\mathbf{\bar{g}}+\mathbf{Q}_{g}\mathbf{c}\end{array}$$ (4)

where the model parameters $\mathbf{c}$ control the shape and texture simultaneously and $\mathbf{Q}_{s}$, $\mathbf{Q}_{g}$ are matrices describing the modes of variation derived from the training set. The effect of varying one element of $\mathbf{c}$ for a model built from a set of 2D MR brain image is shown in Fig. 1.