Extremely Optimized DRLSE Method and Its Application to Image Segmentation
Dengwei Wang · IEEE Access · 2019
This paper proposes an extremely optimized distance regularized level set evolution (EO-DRLSE) method for image segmentation applications within the framework of level set method (LSM), which combines various types of local statistical features and adopts an adaptive regularization strategy which specifically serves for zero level set. Firstly, a variable coefficient for the contour length regularization term is designed based on the local normalized entropy, which can adaptively refreshes its value with the change of local disturbance characteristics of the image. Secondly, based on the local fitting means, the constant coefficient of the region term of the original DRLSE model is modified as an adaptive variable with its value changes as the local fitting mean changes. Thirdly, an improved edge stop function is constructed based on the local fitting variances, which enables the evolution process to maintain a considerable evolution speed in the non-target noise interference position without stopping. Obviously, this gives our algorithm a particularly strong noise suppression ability. Fourthly, an additional regularization term that only deals with the zero level curve is added besides the original regularization scheme (the whole level set function (LSF) is regularized). The extended combined regularization strategy further enhances the noise suppression ability and the adaptability to weak edges of the proposed model. Fifthly, two implementation strategies named morphological snakes and significant target detection mechanism are used to ensure the rapidity and automation of the evolution process. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance in terms of accuracy of segmentation results, rapidity of evolution process, robustness against noise, adaptability to weak target edges. In addition, the factors that can have a key impact on segmentation performance are also analyzed in depth.