面向荧光显微图像的斑点检测
Blob Detection for Fluorescence Microscopy Image
何柯材 1徐琳 1江金康 2陶禹川 1王学渊1
作者信息
- 1. 西南科技大学信息工程学院,绵阳 621000
- 2. 北京化工大学经济管理学院,北京 100029
- 折叠
摘要
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.
Abstract
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.
关键词
荧光显微图像/斑点检测/图像降噪/图像增强Key words
fluorescence microscopy image/blob detection/image denoising/image enhancement引用本文复制引用
基金项目
四川省自然科学基金(2023NSFSC0466)
出版年
2024
NETL
NSTL
万方数据