Automated Breast Mass Segmentation using Pulse-Coupled Neural Network and Distance Regularized Level Set Evolution: A Coarse-to-fine Approach

Motivation: Computer-aided diagnosis (CAD) is an important means for the clinical detection of breast cancer. Mass is a common manifestation of breast cancer. This work aims to develop an effective breast mass segmentation algorithm for CAD systems. Method: On one hand, pulse-coupled neural network (PCNN) and level set (LS) method have complementary advantages in image segmentation, we therefore combine PCNN and LS. On the other hand, traditional LS method formulates the evolution of the contour through the evolution of a level set function (LSF), and LSF typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. So we use an improved LS model, named distance regularized level set evolution (DRLSE), to achieve desirable segmentation performance. Specifically, we extract the region of interest (ROI) with PCNN and sets initial contour for DRLSE first. Then the finely segmentation is achieved by DRLSE. Results: Both qualitative and quantitative experiments on three large-scale mammography databases prove that the proposed method achieves high segmentation accuracy. Conclusion: The proposed algorithm is effective for automatic breast mass segmentation. Significance: First, the sketchy position of mass is fixed by PCNN, which guides the algorithm to define a flexibly initial contour for DRLSE. This strategy makes it easier for the contour to move from initial position towards the boundary between mass and normal tissue. Second, the use of DRLSE, which introduces an intrinsic capability of maintaining regularity of the LSF, ensures stable LS evolution and achieves accurate segmentation.