面向荧光显微图像的斑点检测

实体肿瘤学中, 利用荧光原位杂交(FISH)技术处理后的间期细胞核荧光显微图像上, DNA扩增往往呈现为衍射极限斑点, 成像条件限制了图像质量, 导致图像信噪比较低、背景干扰严重且存在非斑点结构干扰. 设计适用的斑点检测方法, 提供客观且定量的数据, 有助于医生对于癌症病情的诊断. 算法首先采用3层小波多尺度求和对荧光图像去噪, 随后利用多尺度高斯拉普拉斯算子增强斑点区域, 最后通过4个方向的单边二阶高斯核抑制非斑点区域, 完成斑点检测. 实验结果表明, 对于自建数据库中83张图像, 算法平均F分数达到0.96, 平均运行时间0.5 s以下.

Blob Detection for Fluorescence Microscopy Image

In solid oncology, on fluorescence microscopy images of interphase nuclei processed with fluorescence in situ hybridization (FISH) technology, DNA amplification often appears as diffraction-limited blobs. Imaging conditions limit image quality, resulting in a low image signal-to-noise ratio of the image, serious background interference, and non-blob structure interference. Designing suitable blob detection methods to provide objective and quantitative data helps doctors diagnose cancer. The algorithm first uses three-layer wavelet multiscale summation to denoise the fluorescence image, then uses the multiscale Laplacian of Gaussian operator to enhance the blob area, and finally suppresses the non-blob area through unilateral second-order Gaussian kernels in four directions to complete blob detection. Experimental results show that for 83 images in the self-built database, the average F-score reaches 0.96, and the average running time is less than 0.5 s.