芦碧波, 王培, 刘利群, 王永茂, 张自豪, 林忠华. 高污染基因芯片图像的网格划分[J]. 应用光学, 2019, 40(1): 73-78.
引用本文: 芦碧波, 王培, 刘利群, 王永茂, 张自豪, 林忠华. 高污染基因芯片图像的网格划分[J]. 应用光学, 2019, 40(1): 73-78.
LU Bibo, WANG Pei, LIU Liqun, WANG Yongmao, ZHANG Zihao, LIN Zhonghua. Gridding dividing of high polluted gene chip image[J]. Journal of Applied Optics, 2019, 40(1): 73-78.
Citation: LU Bibo, WANG Pei, LIU Liqun, WANG Yongmao, ZHANG Zihao, LIN Zhonghua. Gridding dividing of high polluted gene chip image[J]. Journal of Applied Optics, 2019, 40(1): 73-78.

高污染基因芯片图像的网格划分

Gridding dividing of high polluted gene chip image

  • 摘要: 对基因芯片图像进行网格划分是基因芯片图像处理的基础,针对高污染基因芯片图像中高亮污渍对网格划分造成的干扰,将高亮目标分为靶点、污渍块和污渍点3种类型,根据其不同特征分别进行处理,提出一种新的网格划分算法。利用全局分割确定高亮目标所在位置,根据污渍点的特征,使用图像腐蚀技术对其进行剔除;根据污渍块的特征,对其先进行膨胀处理,然后对其进行剔除,可消除污渍块及其边缘痕迹。使用自协方差对没有污渍的图像进行网格划分,实验表明,对于高污染基因芯片图像,该算法可以得到理想的网格划分结果,靶点检测平均准确率可以达到94.73%。

     

    Abstract: The gridding is a basic step in the gene chip image processing. To suppress the influence of the highlight stains which could interfere with the gridding of the high pollution gene chip images, the highlight objects were divided into three categories: spots, stain blocks and stain points. Based on its characteristics, every category was processed respectively and a new gridding algorithm was proposed. Firstly, the highlight objects were located by utilizing a global thresholding segmentation. Secondly, the strain spots were smoothed via image corrosion technology. The strain blocks were removed using a dilation technology to reduce the trace of edges. Finally, the autocovariance was utilized for gridding the noise-free gene chip images. Experimental results show that the proposed algorithm can obtain an ideal gridding result for the high pollution gene chip images and the spot detection average accuracy can reach 94.73%.

     

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