Intarapanich Apichart, Kaewkamnerd Saowaluck, Pannarut Montri, Shaw Philip J, Tongsima Sissades
National Electronics and Computer Technology Center, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand.
National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand.
BMC Bioinformatics. 2016 Dec 22;17(Suppl 19):516. doi: 10.1186/s12859-016-1373-2.
Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue.
The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time.
We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time.
This work presents a modification of the extended depth of field approach for efficiently enhancing microscopic images. This selective object processing scheme used in OEDoF can significantly reduce the overall processing time while maintaining the clarity of important image features. The empirical results from parasite-infected red cell images revealed that our proposed method efficiently and effectively produced in-focus composite images. With the speed improvement of OEDoF, this proposed algorithm is suitable for processing large numbers of microscope images, e.g., as required for medical diagnosis.
显微镜分析要求感兴趣的前景物体（如细胞）处于对焦状态。在典型的显微镜标本中，前景物体可能位于不同的景深上，因此需要采集在不同焦平面拍摄的多张图像。扩展景深（EDoF）技术是一种计算方法，用于将来自不同景深的图像合并成一张所有前景物体都对焦的合成图像。EDoF生成的合成图像可应用于自动图像处理和模式识别系统。然而，当前的EDoF算法计算量很大且不实用，特别是对于像医学诊断这样对样本快速周转很重要的应用。由于前景物体通常只占图像的一小部分，如果只处理前景区域来生成合成图像，EDoF技术可以工作得快得多。我们提出了一种名为基于对象的扩展景深（OEDoF）的新算法来解决这个问题。
OEDoF算法由四个主要模块组成：1）颜色转换，2）对象区域识别，3）良好对比度像素识别和4）细节合并。首先，该算法采用颜色转换来增强对比度，然后识别前景像素。仅使用这些前景像素构建合成图像，这大大减少了计算时间。
我们使用从45个确诊疟疾感染标本中获得的250张图像来测试我们提出的算法。使用提出的OEDoF算法生成了所有对焦物体的合成图像。我们从图像清晰度（质量）和处理时间方面测量了OEDoF的性能。OEDoF算法选择的感兴趣特征在质量上与由最先进的复小波EDoF算法处理的图像中的等效区域相当；然而，OEDoF所需的处理时间少四倍。
这项工作提出了一种扩展景深方法的改进，以有效地增强显微图像。OEDoF中使用的这种选择性对象处理方案可以显著减少整体处理时间，同时保持重要图像特征的清晰度。来自寄生虫感染红细胞图像的实证结果表明，我们提出的方法高效且有效地生成了对焦的合成图像。随着OEDoF速度的提高，该算法适用于处理大量显微镜图像，例如医学诊断所需的图像。
