Application of robust sequential edge detection and linking to boundaries of low contrast lesions in medical images

An algorithm for the optimal edge contour estimation in medical images without a priori shape models is described. The proposed method is an intermediate-level approach compared with the usual low-level edge-detection operators. An optimal and robust contour estimator is derived by the minimization of a risk function which measures the error from both an inappropriate choice of edge contour and an inappropriate choice of the noise model in the image. The result includes Huber´s function. If a parametric statistical noise model and the Neyman-Pearson criterion are used, the result is an extension of maximum-likelihood function. A recursive formulation can be implemented by assuming an independent random field and a Markov path model. The assumption of independent statistics can be satisfied by the use of an autoregressive moving-average preprocessor. The problem of varying edge strength is lessened using an adaptive trimmed mean. The robust algorithm is implemented using a priority-tree (stack) structure. The system´s performance is illustrated by estimation of lesion boundaries in medical images