目的 目前，许多图像分割算法对含有丰富纹理信息的图像的分割效果并不理想，尤其是在不同纹理的边缘信息的保持方面。为了解决这一问题，提出一种基于连续纹理梯度信息的各向异性图像分割算法。方法 在分水岭算法的基础上，引入纹理梯度各向异性算法，能够在避免纹理信息影响分割效果的前提下，最大限度地保证纹理边缘信息的完整。针对纹理特征数据敏感的特性，本文将离散的图像高度信息映射到连续的纹理梯度空间，能够有效减少由细小差异造成的过分割现象。结果 本文方法在BSD500 Dataset和Stanford Background Dataset中选择了大量的纹理信息丰富的图片与最新的分割算法进行了实验与对比。本文方法在分割效果（降低过分割现象）、保持边缘信息和分割准确率等方面均获得明显改进，并在图像分割的平均准确率方面与最新算法进行比较发现，本文算法的平均分割准确率达到90.9%，明显超过了其他最新算法，验证了本文方法的有效性。结论 本文提出的基于分水岭的纹理梯度各向异性算法对纹理图像的分割具有保边和准确的特点，采用连续梯度空间的方法能够有效地减少传统分水岭算法的过分割现象。本文方法主要适用于纹理信息丰富（自然纹理和人工纹理）的图片。

Objective Currently, the efficiency and effectiveness of several existing segmentation algorithms on images with rich texture information are not satisfactory, especially at the object edges between different texture areas. Texture information will disrupt the performance of traditional segmentation algorithms. However, the textures prevail in major images, such as the grass, trees, sky, and sea. The segmentation of texture images without losing edge information becomes crucial. To solve this problem, this paper proposes a novel anisotropic image segmentation algorithm based on continuous texture gradients to reduce the influence of texture areas. Method An improved watershed(WS) algorithm is proposed to preserve accurate edge data. The benefits of using the WS algorithm are the feature of edge preservation. However, the texture in the different areas of images will reduce the accuracy of the WS algorithm segmentation. Inspired by the traditional anisotropic filtering approaches, this paper presents an improved anisotropic texture gradient, which could detect a different area with a different texture and preserve the sharpness of texture edges. The combination of the WS algorithm and the anisotropic texture gradients maintains the completeness of the edge information among the different texture areas without losing the effect of the segmentation of texture areas. We extend the single-channel color computation of the WS algorithm to three channels(RGB) for improved performance on color image operation. The proposed method maps the altitude information into the continuous texture space by using log function to manage the data sensitivities of texture features. Therefore, the continuous texture space is beneficial for water widget judgment and has good performance on the over-segmentation because of applying the proposed texture anisotropic algorithm in the continuous texture gradient space. Result We conduct experiments to compare the texture segmentation results with other classical filter operators, such as Gaussian and mean filters and to compare the two newest segmentation approaches on the segmentation accuracy. The datasets used are the BSD500 and the Stanford background datasets, and we select numerous images with rich texture information. After the experiments, the edges filtered by Gaussian and mean filter are all blurred, and thus it is difficult to perform segmentation. However, our proposed anisotropic texture filtering method performs well on edge preservation while removes texture information from object images. Simultaneously, the results indicated that the proposed algorithm has obvious advantages on accuracy and edge conservation compared with the newest approaches. We compute the average accuracy of segmentation to evaluate the results quantitatively. The average accuracy of our method has reached nearly 90.9%, obviously exceeding other recent algorithms. This result verifies the effectiveness of our method. Conclusion The proposed algorithm has advantages of edge preserving and high accuracy with less over-segmentation. The feasibility of the algorithm is proven through comparison with the latest segmentation algorithms. Our method is suitable for images with rich texture information, such as natural and artificial textures.