Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster

Due to the low contrast, blurred boundary and intensity inhomogeneity of images, accurate segmentation breast cancer lesions with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) still has great challenges. This paper proposed an improved active contour model (ACM) for segmenting in DCE-MRI images. First, based on extreme learning machine (ELM) method, a robust function is that combines image intensities time-domain features enhance difference between other tissues. Second, edge-stop (ESF) introduced by combining intensity, feature, Hessian shape index detect irregular boundaries. At lesions, energy ACM minimized evolution curve completes, so lesion region can be segmented. The mean Dice similar coefficient (DICE), Jaccard similarity (JC) Hausdorff distance (HD) 50 samples are 85.88±6.62%, 75.72±9.68% 11.62±4.72 mm, respectively. results segmented more manual than compared models.