1. An image data processor, comprising: a surface roughness quantifier that is configured to generate a roughness metric that quantifies a roughness of a surface of a tissue of interest in 3D image data based on a surface model adapted to the tissue of interest in the 3D image data, wherein generating the roughness metric comprises: calculating a position variation of a pre-defined contrast gradient with respect to a mean surface position along a direction normal to the model surface; and determining an integral of a local position variance of the gradient, normalized to a total tissue of interest surface; and a decision component that is configured to generate a value signal indicating a presence or an absence of disease in the tissue of interest based on the roughness metric, wherein generating the value signal comprises: comparing the roughness metric to a predetermined threshold; generating a first value of the value signal if the roughness metric exceeds the predetermined threshold; and generating a second value of the value signal if the roughness metric does not exceed the predetermined threshold.