A fast auto-focusing method of microscopic imaging based on an improved MCS algorithm

Abstract

An improved \three steps" mountain-climb searching (MCS) algorithm is proposed which is applied to auto-focusing for microscopic imaging accurately and e±ciently. By analyzing the performance of several evaluation functions, the variance function and the Brenner function are synthesized as a new evaluation function. In the¯rst step, a self-adaptive step length which is much dependent on the reciprocal of the evaluation function value at the beginning position of climbing is used for approaching the halfway up the mountain roughly. Secondly, a¯xed moderate step length is applied for approaching the mountaintop of the variance function as closer as possible. Finally, ā ne step is employed for reaching the exact mountaintop of the Brenner function. The microscope auto-focusing experiments based on the proposed algorithm for blood smear detection have been carried out comprehensively. The results show that the improved algorithm can not only guarantee the precision to get clear focal images, but also improve the auto-focusing e±ciency.

An Auto-Focus Method of Microscope for the Surface Structure of Transparent Materials under Transmission Illumination

“…In particular, image sharpness measurements based on the spatial domain are widely used due to their simple calculation and good stability. For focus search algorithms, many results are derived based on hill-climbing search [ 11 ], Fibonacci search [ 12 , 13 ], evolutionary computation [ 14 ], and mathematical model [ 15 , 16 ].…”

An Overlapping and Integral Blurry Evaluation Method to Optimize Tissues Scanning from Microscope

“…The detection of the blurry zones is accomplished by the selection of an adequate focus measure. Many subroutines were found in the literature to calculate the focus, such as Tenengrad function, energy gradient function, Brenner function, and Entropy function [13][14][15][16][17][18][19][20][21]. The evaluation method should be selected based on its performance, unbiasedness, higher signal to noise ratio [11] [18] [22].…”

Time-controlled Multichannel Dynamic Traction Imaging of Biaxially Stretched Adherent Cells

“…In the offline step (Figure 1b), the algorithm performs a Z-axis sweep with 100 nm steps around the focus of the sample-specific surface region and extracts an image contrast-based sharpness value for each plane through the Brenner function (used in autofocusing approaches) using only the center (174 x 130 pixels) of the binned images. [14,15] Considering the position of the desired focus plane as reference, a relation between the distance from the desired focus plane and the Brenner (sharpness) value [BV] can be defined. The new algorithm assumes that the distance-sharpness curve is symmetric around the focus plane with the maximum BV, which is true for the surface-aligned beads.…”

“…To minimize lateral tracking error, we used a closed loop controller implemented with a Smith Predictor to manage the time delays caused by camera, stage and offset calculation, for the lateral compensation. [15] For the axial compensation, images were only considered after each finished focus adjustment.…”

“…The sample-specific information-extended algorithm eliminates the iteration extensive (mountain climbing) focus search by a precise estimation of required stage correction with accuracy of a few hundred nm. [15] The generality of such focusing algorithm enables it to be used with any fast fluorescence microscope technique where the Brenner function-based interpretation of sample sharpness gives quasi-symmetric sharpnessdistance curve around the desired focus plane.…”