Experimental and theoretical investigations of signal detection in medical imaging have been increasingly based on realistic images. In this presentation, techniques for producing realistic breast tumor masses and microcalcifications will be described. The mass lesions were obtained from 24 specimen radiographs of surgically removed breast tissue destined for pathological evaluation. A variety of masses were represented including both lobular and spiculated ductal carcinomas as well as fibroadenomas. Mass sizes ranged from 4 to 18 mm. The specimens included only a small amount of attached normal tissue, so tumor boundaries could be identified subjectively. A simple, interactive quadratic surface generating method was used for background subtraction -- yielding an isolated tumor image. Individual microcalcifications were generated using a 3D stochastic growth algorithm. Starting with a central seed cell, adjacent cells were randomly filled until the 3D object consisted of a randomly selected number of filled cells. The object was then projected to 2D, smoothed and sampled. It is possible to generate a large variety of realistic shapes for these individual microcalcifications by varying the rules used to control stochastic growth. MCCs can then randomly generated, based on the statistical properties of clusters described by LeFebvre et al.
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