Optimal segmentation of cell images
Optimal segmentation of cell images
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DOI：
10.1049/ip-vis:19981690
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发表时间：
1998-02
期刊：
影响因子：
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通讯作者：
Haisang Wu
中科院分区：
文献类型：
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作者：
Haisang Wu
提出了一种光镜细胞图像的优化分割算法。通过对近似原始图像的参数图像进行阈值化来执行图像分割。使用原始图像和构造图像之间的均方误差作为成本函数，分割问题被转换成一个优化过程，其中确定参数参数，使定义的成本函数最小化。使用无监督学习规则来调整参数，迭代地最小化成本函数，并且基于所获得的参数，在每次迭代时构造参数图像。通过对最终参数图像进行阈值化来提取细胞区域，其中阈值是图像参数之一。真实的宫颈图像的应用结果显示所提出的分割方法的性能。实验分割结果提出了建议的最佳算法的合成细胞图像被不同程度的噪声破坏，这些结果进行了比较，与K-均值聚类方法和贝叶斯分类器的分类错误。
An optimal segmentation algorithm for light microscopic cell images is presented. The image segmentation is performed by thresholding a parametric image approximating the original image. Using the mean squared error between the original and the constructed image as the cost function, the segmentation problem is transformed into an optimisation process where parametric parameters are determined that minimise the defined cost function. The cost function is iteratively minimised using an unsupervised learning rule to adjust the parameters, and a parametric image is constructed at each iteration, based on the obtained parameters. The cell region is extracted by thresholding the final parametric image, where the threshold is one of the image parameters. Application results to real cervical images are provided to show the performance of the proposed segmentation approach. Experimental segmentation results are presented for the proposed optimal algorithm for synthetic cell images corrupted by variant levels of noise; these results are compared with the K-means clustering method and Bayes classifier in terms of classification errors.