AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.

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Following homozygosity mapping in a single kindred, we identified nonsense and missense mutations in MYO5B, encoding type Vb myosin motor protein, in individuals with microvillus inclusion disease (MVID). MVID is characterized by lack of microvilli on the surface of enterocytes and occurrence of intracellular vacuolar structures containing microvilli. In addition, mislocalization of transferrin receptor in MVID enterocytes suggests that MYO5B deficiency causes defective trafficking of apical and basolateral proteins in MVID.

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We describe 'Active Shape Models' which iteratively adapt to refine estimates of the pose, scale and shape of models of image objects. The method uses flexible models derived from sets of training examples. These models, known as Point Distribution Models, represent objects as sets of labelled points. An initial estimate of the location of the model points in an image is improved by attempting to move each point to a better position nearby. Adjustments to the pose variables and shape parameters are calculated. Limits are placed on the shape parameters ensuring that the example can only deform into shapes conforming to global constraints imposed by the training set. An iterative procedure deforms the model example to find the best fit to the image object. Results of applying the method are described. The technique is shown to be a powerful method for refining estimates of object shape and location.

for the Gene Modifier Study Group C YSTIC FIBROSIS (CF) IS A REcessive monogenic disorder characterizedbymultiorganinvolvement and clinical heterogeneity that is incompletely explained by mutations within the cystic fibrosis transmembraneconductanceregulator(CFTR) gene (OMIM 602421). 1 Patients with CF, including those homozygous for DF508, smallfraction(Ϸ3%-5%)ofpatientswith CF develops severe liver disease characterized by cirrhosis with portal hypertension (CFLD) 1 ; thus, non-CFTR genetic variability may contribute to risk for severe liver disease. [14][15][16][17] To determine the association between non-CFTR genetic polymorphisms and CFLD, we studied 9 functional variants in 5 genes previously See also Patient Page.

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