Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.

A Multi-Scale Information Fusion Level Set for Breast Tumor Segmentation

Purpose: Mammography is considered an effective method of examination in early breast cancer screening. Massive work by distinguished researchers of breast segmentation has been proposed. However, due to the blurry boundaries of the breast tumor, the variability of its shape and the overlap with surrounding tissue, the breast tumor’s accurate segmentation still is a challenge. Methods: In this paper, we proposed a novel level set model which based on the optimized local region driven gradient enhanced level set model (OLR-GCV) to segment tumor within a region of interest (ROI) in a mammogram. Firstly, Noise, labels and artifacts are removed from breast images. The ROI is then obtained using the intuitionistic fuzzy C-means method. Finally we used OLR-GCV method to accurately segment the breast tumor. The OLR-GCV model combines regional information, enhanced edge information and optimized Laplacian of Gaussian (LOG) energy term. The regional and enhanced edge information are used to capture local, global and gradient information of breast images. The optimized Laplacian of Gaussian (LOG) energy term is introduced in the energy functional to further optimize edge information to improve segmentation accuracy. Results: We evaluated our method on the MIAS and DDSM datasets. It yielded a Dice value of 96.86% on the former and 95.51% on the latter. Our method proposed achieves higher accuracy of segmentation than other State-of-the-art Methods. Conclusions: Our method has better segmentation performance, and can be used in clinical practice.

LSM-SEC: Tongue Segmentation by the Level Set Model with Symmetry and Edge Constraints

Accurate segmentation of the tongue body is an important prerequisite for computer-aided tongue diagnosis. In general, the size and shape of the tongue are very different, the color of the tongue is similar to the surrounding tissue, the edge of the tongue is fuzzy, and some of the tongue is interfered by pathological details. The existing segmentation methods are often not ideal for tongue image processing. To solve these problems, this paper proposes a symmetry and edge-constrained level set model combined with the geometric features of the tongue for tongue segmentation. Based on the symmetry geometry of the tongue, a novel level set initialization method is proposed to improve the accuracy of subsequent model evolution. In order to increase the evolution force of the energy function, symmetry detection constraints are added to the evolution model. Combined with the latest convolution neural network, the edge probability input of the tongue image is obtained to guide the evolution of the edge stop function, so as to achieve accurate and automatic tongue segmentation. The experimental results show that the input tongue image is not subject to the external capturing facility or environment, and it is suitable for tongue segmentation under most realistic conditions. Qualitative and quantitative comparisons show that the proposed method is superior to the other methods in terms of robustness and accuracy.