A method for integration of medical diagnostic data and a geometric model of a movable body based on a first dataset and a second dataset, said method comprising the steps of: - segmenting an image dataset representative of at least a geometry of the movable body, wherein for the movable body a body with pre-determined shape and / or motion parameterization is selected; - segmenting the image dataset using said shape and / or motion parameterization; - selecting for the first dataset results of the segmentation step of the image dataset representative of a geometry of the movable body, said first dataset comprising shape and motion parameters, the shape parameters representing a particular instance of the movable body, for example at the beginning of the motion, and the motion parameters describing how to deform this initial shape, wherein the image dataset comprises a functional image dataset comprising information on anatomy; - selecting for the second dataset a texture image dataset comprising said medical diagnostic data about the movable body different from that of the image dataset, wherein the texture image dataset is computed on the basis of a contrast-enhanced image dataset comprising information on physiology; - constructing the geometric model of the movable body based on said results of the segmentation step, whereby said geometric model is representative of the shape and motion of the movable body, whereby for the geometric model a deformable geometric model representative of the shape and the motion of said movable body is selected; - determining a spatial correspondence information between the results of the segmentation step and the texture image dataset, wherein the determination of the spatial correspondence is obtained by segmenting the texture image dataset using the shape and / or motion parameterization of the movable body; - fusing the geometric model and the texture image dataset using said spatial correspondence information yielding a textured geometric model of the movable body. - segmenting an image dataset representative of at least a geometry of the movable body, wherein for the movable body a body with pre-determined shape and / or motion parameterization is selected; - segmenting the image dataset using said shape and / or motion parameterization; - selecting for the first dataset results of the segmentation step of the image dataset representative of a geometry of the movable body, said first dataset comprising shape and motion parameters, the shape parameters representing a particular instance of the movable body, for example at the beginning of the motion, and the motion parameters describing how to deform this initial shape, wherein the image dataset comprises a functional image dataset comprising information on anatomy; - selecting for the second dataset a texture image dataset comprising said medical diagnostic data about the movable body different from that of the image dataset, wherein the texture image dataset is computed on the basis of a contrast-enhanced image dataset comprising information on physiology; - constructing the geometric model of the movable body based on said results of the segmentation step, whereby said geometric model is representative of the shape and motion of the movable body, whereby for the geometric model a deformable geometric model representative of the shape and the motion of said movable body is selected; - determining a spatial correspondence information between the results of the segmentation step and the texture image dataset, wherein the determination of the spatial correspondence is obtained by segmenting the texture image dataset using the shape and / or motion parameterization of the movable body; - fusing the geometric model and the texture image dataset using said spatial correspondence information yielding a textured geometric model of the movable body.