# Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications
## 3 Intracellular Micromanipulation with Computational Image Information
### Depth Acquisition
#### 3.2.2 Autofocusing  
In comparison with the template matching method that involves the use of a predefined image to estimate the target height, the autofocusing method is utilized to analyze depth information based on imaging characters from sampled images. In imaging practices, the light intensity of a target edge experiences an abrupt change during the focal plane movement. Therefore, imaging depth can be obtained by searching sampled images from the focused plane. Based on this idea, many attempts called depth from focus (DFF) methods have been applied. An entropy computation approach was designed to focalize micropipette tips [153]. In this method, the rectangular region of the micropipette tip was first selected. When the tip was focused, the entropy value in this region reached its maximum clarity.  
Figure 8: Template matching methods. (**a**) Optimized template matching method with gradient differences. Adapted with permission from Reference [148]; (**b**) Three-frame template matching method. Adapted with permission from Reference [151].  
A Prewitt operator was proposed to highlight cell edges of sampled images [154]. The processed pixel points exhibit higher intensity for sharper edges. In this way, the pixel intensity of the processed images was counted, and the focal plane was located in the plane with the largest amount, as shown in Figure 9a. However, out-of-focus blurs in sampled images were hardly removed with the Prewitt operator and even produced artifacts in highlighted edges. Currently, another micropipette autofocusing method called depth from border intensity variation has been created [155; 156]. In this method, a stripe near the target object boundary was defined to compensate for the inaccurate boundaries and imaging noises. A focal plane can be selected by using a proposed defocus function from a Fourier spectrum, as presented in Figure 9b. DFF methods are time consuming because a large number of sampled images are required. For depth from defocus (DFD) methods, which only examine a limited number of images, the detection efficiency is greatly improved. In the proposed multiple DFD method [157; 158], microscope imaging behavior was expressed as a simplified fixed Gaussian model. The depth of the current imaging plane can be calculated from the cell diameter and image depth in two other sampled images, as illustrated in Figure 9c. The authentic light spread function is space-variant because of the spherical aberrations caused by the mismatch of refractive indices between a lens immersion medium and a specimen and the discrepancy between the ideal and real parameters of the microscope system. Therefore, a fixed model is inaccurate for most specimens. Meanwhile, the cell diameter in this approach was derived from image segmentation by maximizing the posteriori estimation. The actual cell boundary was hardly extracted under the interferences of out-of-focus blurs and noises.  
Figure 9: Autofocusing methods. (**a**) An autofocusing method based on a Prewitt operator. Adapted with permission from Reference [154]; (**b**) An autofocusing method based on the depth from border intensity variation. Adapted with permission from Reference [155]; (**c**) Multiple depth estimation from defocus method. Adapted with permission from Reference [157].