Autofocusing in microscopy systems using graphics processing units

Valdiviezo-N., Juan C.; Hernández-Tapia, Jorge; Mera-González, Laura; Toxqui-Quitl, C.; Padilla-Vivanco, Alfonso

doi:10.1117/12.2024967

Cited by 4 publications

(3 citation statements)

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“…We remark that the metrics that were selected for this study have shown promising results under different microscopy modalities for the autofocus process of biological samples in preliminary studies. 10,22 With the aim of presenting the mathematical description of such algorithms, consider an image g from which we want to compute an AF , and whose intensity levels at coordinates i; j, for i ¼ 1; : : : ; M and j ¼ 1; : : : ; N, respectively, are represented as gði; jÞ. In the following description, the operator ⊗ denotes the usual convolution operation, and T denotes matrix transposition.…”

Section: Mathematical Description Of Autofocus Functionsmentioning

confidence: 99%

Parallel implementations to accelerate the autofocus process in microscopy applications

2020

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Several autofocus algorithms based on the analysis of image sharpness have been proposed for microscopy applications. Since autofocus functions (AFs) are computed from several images captured at different lens positions, these algorithms are considered computationally intensive. With the aim of presenting the capabilities of dedicated hardware to speed-up the autofocus process, we discuss the implementation of four AFs using, respectively, a multicore central processing unit (CPU) architecture and a graphic processing unit (GPU) card. Throughout different experiments performed on 300 image stacks previously identified with tuberculosis bacilli, the proposed implementations have allowed for the acceleration of the computation time for some AFs up to 23 times with respect to the serial version. These results show that the optimal use of multicore CPU and GPUs can be used effectively for autofocus in realtime microscopy applications.

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Section: Mathematical Description Of Autofocus Functionsmentioning

confidence: 99%

Parallel implementations to accelerate the autofocus process in microscopy applications

2020

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“…Both methods, DHM and OR-MAP, mainly rely on FFT transforms which requires O(M * N * log(M * N)) operations, being M * N the size of the image, therefore massively parallel GPUs are needed to process even low resolution images. Also First-Derivative methods have taken advantage of GPU architectures showing promising results [10]. Since these methods can be implemented by means of 2D convolutions, the computational complexity can be limited to O(M * N * k * k), being k the kernel size, or even O(M * N * 2 * k) in the case of separable filters.…”

Section: Introductionmentioning

confidence: 99%

Autofocus method for automated microscopy using embedded GPUs

Castillo-Secilla

Saval-Calvo

Medina-Valdés

et al. 2017

Biomed. Opt. Express

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Abstract:In this paper we present a method for autofocusing images of sputum smears taken from a microscope which combines the finding of the optimal focus distance with an algorithm for extending the depth of field (EDoF). Our multifocus fusion method produces an unique image where all the relevant objects of the analyzed scene are well focused, independently to their distance to the sensor. This process is computationally expensive which makes unfeasible its automation using traditional embedded processors. For this purpose a low-cost optimized implementation is proposed using limited resources embedded GPU integrated on cutting-edge NVIDIA system on chip. The extensive tests performed on different sputum smear image sets show the real-time capabilities of our implementation maintaining the quality of the output image.

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“…Due to the high computational effort, these methods have been implemented in parallel computer systems such as clusters and GPUs. [21][22][23] In this work, a parallel implementation in GPU of a pixel-by-pixel image fusion of multifocus color images based on MGC is done. According to the image quality metrics, the proposed method is competitive to merge these kinds of images.…”

Section: Introductionmentioning

confidence: 99%

Focus measure method based on the modulus of the gradient of the color planes for digital microscopy

et al. 2018

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, "Focus measure method based on the modulus of the gradient of the color planes for digital microscopy," Opt. Eng. 57(2), 023106 (2018), doi: 10.1117/1.OE.57.2.023106. Abstract. The modulus of the gradient of the color planes (MGC) is implemented to transform multichannel information to a grayscale image. This digital technique is used in two applications: (a) focus measurements during autofocusing (AF) process and (b) extending the depth of field (EDoF) by means of multifocus image fusion. In the first case, the MGC procedure is based on an edge detection technique and is implemented in over 15 focus metrics that are typically handled in digital microscopy. The MGC approach is tested on color images of histological sections for the selection of in-focus images. An appealing attribute of all the AF metrics working in the MGC space is their monotonic behavior even up to a magnification of 100×. An advantage of the MGC method is its computational simplicity and inherent parallelism. In the second application, a multifocus image fusion algorithm based on the MGC approach has been implemented on graphics processing units (GPUs). The resulting fused images are evaluated using a nonreference image quality metric. The proposed fusion method reveals a high-quality image independently of faulty illumination during the image acquisition. Finally, the three-dimensional visualization of the in-focus image is shown. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Autofocusing in microscopy systems using graphics processing units

Cited by 4 publications

References 0 publications

Parallel implementations to accelerate the autofocus process in microscopy applications

Parallel implementations to accelerate the autofocus process in microscopy applications

Autofocus method for automated microscopy using embedded GPUs

Focus measure method based on the modulus of the gradient of the color planes for digital microscopy

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