Zheng Haiyong, Zhao Hongmiao, Sun Xue, Gao Huihui, Ji Guangrong
Department of Electronic Engineering, Ocean University of China, No. 238 Songling Road, Qingdao, Shandong, 266100, China.
Microsc Res Tech. 2014 Sep;77(9):684-90. doi: 10.1002/jemt.22389. Epub 2014 Jun 10.
A novel image processing model Grayscale Surface Direction Angle Model (GSDAM) is presented and the algorithm based on GSDAM is developed to segment setae from Chaetoceros microscopic images. The proposed model combines the setae characteristics of the microscopic images with the spatial analysis of image grayscale surface to detect and segment the direction thin and long setae from the low contrast background as well as noise which may make the commonly used segmentation methods invalid. The experimental results show that our algorithm based on GSDAM outperforms the boundary-based and region-based segmentation methods Canny edge detector, iterative threshold selection, Otsu's thresholding, minimum error thresholding, K-means clustering, and marker-controlled watershed on the setae segmentation more accurately and completely.
提出了一种新型图像处理模型——灰度表面方向角模型（GSDAM），并开发了基于GSDAM的算法，用于从角毛藻显微图像中分割刚毛。该模型将显微图像的刚毛特征与图像灰度表面的空间分析相结合，以从低对比度背景以及可能导致常用分割方法失效的噪声中检测和分割出细长的方向刚毛。实验结果表明，我们基于GSDAM的算法在刚毛分割方面比基于边界和基于区域的分割方法（如Canny边缘检测器、迭代阈值选择、大津阈值法、最小误差阈值法、K均值聚类和标记控制分水岭算法）更准确、更完整。
