Department of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China.
Shandong Provincial Key Laboratory of Digital Media Technology, Jinan 250014, China.
Comput Intell Neurosci. 2021 Jul 29;2021:6370526. doi: 10.1155/2021/6370526. eCollection 2021.
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.
舌体的精确分割是计算机辅助舌诊的重要前提。一般来说，舌的大小和形状差异很大，舌的颜色与周围组织相似，舌的边缘模糊，部分舌受到病理细节的干扰。现有的分割方法往往不能很好地处理舌图像。针对这些问题，本文提出了一种结合舌几何特征的对称和边缘约束水平集模型进行舌分割。基于舌的对称几何形状，提出了一种新的水平集初始化方法，以提高后续模型演化的准确性。为了增加能量函数的演化力，在演化模型中添加了对称检测约束。结合最新的卷积神经网络，得到舌图像的边缘概率输入，以指导边缘停止函数的演化，从而实现准确、自动的舌分割。实验结果表明，所提出的方法不受外部采集设备或环境的影响，适用于大多数实际情况下的舌分割。定性和定量比较表明，该方法在鲁棒性和准确性方面优于其他方法。
