Huang Yali, Liu Zhiwen
Institute of Signal and Image Processing, School of Information and Electronics, Beijing Institute of Technology (BIT), 5 South Zhongguancun Street, Haidian District, Beijing 100081, China ; College of Electronics and Information Engineering, Hebei University, Baoding, China ; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China.
Institute of Signal and Image Processing, School of Information and Electronics, Beijing Institute of Technology (BIT), 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
Comput Math Methods Med. 2015;2015:693484. doi: 10.1155/2015/693484. Epub 2015 May 18.
The paper proposes an improved active contour model for segmenting and tracking accurate boundaries of the single lymphocyte in phase-contrast microscopic images. Active contour models have been widely used in object segmentation and tracking. However, current external-force-inspired methods are weak at handling low-contrast edges and suffer from initialization sensitivity. In order to segment low-contrast boundaries, we combine the region information of the object, extracted by morphology gray-scale reconstruction, and the edge information, extracted by the Laplacian of Gaussian filter, to obtain an improved feature map to compute the external force field for the evolution of active contours. To alleviate initial location sensitivity, we set the initial contour close to the real boundaries by performing morphological image processing. The proposed method was tested on live lymphocyte images acquired through the phase-contrast microscope from the blood samples of mice, and comparative experimental results showed the advantages of the proposed method in terms of the accuracy and the speed. Tracking experiments showed that the proposed method can accurately segment and track lymphocyte boundaries in microscopic images over time even in the presence of low-contrast edges, which will provide a good prerequisite for the quantitative analysis of lymphocyte morphology and motility.
本文提出了一种改进的主动轮廓模型，用于分割和跟踪相差显微镜图像中单个淋巴细胞的精确边界。主动轮廓模型已广泛应用于目标分割和跟踪。然而，当前受外力启发的方法在处理低对比度边缘时能力较弱，且存在初始化敏感性问题。为了分割低对比度边界，我们将通过形态学灰度重建提取的目标区域信息与通过高斯拉普拉斯滤波器提取的边缘信息相结合，以获得改进的特征图，用于计算主动轮廓演化的外力场。为了减轻初始位置敏感性，我们通过进行形态学图像处理将初始轮廓设置在靠近真实边界的位置。所提出的方法在从小鼠血液样本通过相差显微镜获取的活淋巴细胞图像上进行了测试，对比实验结果显示了该方法在准确性和速度方面的优势。跟踪实验表明，即使存在低对比度边缘，所提出的方法也能随时间准确地分割和跟踪显微镜图像中的淋巴细胞边界，这将为淋巴细胞形态和运动性的定量分析提供良好的前提条件。
