Active contour model based on pre- additive bias field fitting image

Abstract

With regards to figure with inhomogeneous intensity, the models based on active contour model have been widely used. Compared with the classic models, this paper proposes an optimized additive model which contains the edge structure and inhomogeneous components. Second, by introducing a novel clustering criterion, the value of the bias field can be estimated before iteration, greatly speeding the evloving process and reducing the calculation cost. Thus, an improved energy function is drawn out. Considering the gradient descent flow formula, a novel error function and adaptive parameter are utilized to improve the performance of the data term. Finally, the proposed regularization terms ensure the evloving process is more efficient and accurate. Owing to the above mentioned improvements, the proposed model in this paper has excellent performance of the segmentation in terms of robustness, effectiveness and accuracy.