机构地区: 华南师范大学计算机学院
出 处: 《贵州师范大学学报(自然科学版)》 2010年第1期62-65,共4页
摘 要: 为了提取人脑CT图像中的脑部组织,提出了一种改进的分水岭算法,首先采用K-means聚类算法对图像进行初始分割,从而有效地抑制了由图片表面的灰度变化引起的过分割,使边缘定位更加准确;然后在聚类图像的梯度图上利用自动阈值法增强其对比度,进行分水岭分割。最后为了避免过分割现象,对分割后的图像进行了相似区域合并。实验表明该方法简单有效,能够得到符合人类视觉系统特性的分割结果。 In order to extract brain tissue from human brain CT image,we propose an improved watershed algorithm. Firstly, we use K-means clustering to produce a primary segmentation, thus effectively inhibiting over-segmentation caused by grey level change of the surface of picture, which can be located at the contour more accurately. Then the improved watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map . Finally, regions merging algorithm is applied to avoid over-segmentation. The experimental results demonstrate the simplicity and effectivelness of the approach, in accordance with our understanding on the semantic concepts and the perceptual mechanism of human eyes.
领 域: [自动化与计算机技术] [自动化与计算机技术]