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

Abstract

, "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.

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

“…In addition, in order to analyze and view the results more conveniently and intuitively, this study normalizes all the results, as shown in Figure 5 . The non-subsampled contourlet transform (NSCT), Tenengrad algorithm [ 29 ], Roberts algorithm [ 30 , 31 ], discrete cosine transform (DCT) [ 32 , 33 ], energy of gradient (EOG) [ 34 ] algorithm, Canny algorithm [ 35 , 36 ], and Laplacian algorithm [ 37 , 38 ] are selected for this comparative experiment. The Tenengrad algorithm uses a Sobel [ 39 , 40 ] operator to extract gradient values in horizontal and vertical directions, and then calculates the gradient square sum of all pixels.…”

Directional Ring Difference Filter for Robust Shape-from-Focus

“…However, the results also indicate that increasing the sizes of the filters for both RDF and DRDF leads to a greater deviation from the ground truth and, hence, results in a loss of details. Next, the proposed method was compared to seven focus computation methods, i.e., gray-level variance (GLV) [7], modulus of the gradient of the color channel (MCG) [13], modified-Laplacian (ML) [3], sum and spread focus measure (FMSS) [23], reduced Tenengrad (RT) [14], multi-scale-morphological focus measure (MSM) [24], and ring difference filter (RDF) [25]. For visual comparisons, we constructed depth maps of synthetic datasets, Antinous, Cotton, and Pens, using different methods, as shown in Figure 5.…”

“…Tenengrad focus measure (TFM) is the most commonly used; it calculates the gradient of an image using the Sobel operator in both the x and y directions, and then sums up the squared magnitude of the gradient over a small window to obtain the sharpness value. Other common methods in this category include the local edge gradient analysis [12], modulus of the gradient of the color channel (MCG) [13], and reduced Tenengrad (RT) [14], which is a slight modification to the TFM. Second derivative-based focus measures utilize the Laplacian of an image.…”

Enhancing Focus Volume through Perceptual Focus Factor in Shape-from-Focus

“…In the proposed method, first, initial focus volume is obtained through the DRDF and then depth maps are computed from the enhanced focus volume through PFF. The performance of the proposed method is then thoroughly tested against five state-of-the-art methods: GLV [39], MCG [40], ML [24], FMSS [41], and RDF [34]. These five methods are applied to the five real data sets to obtain the focus volumes, and the depth maps are extracted by obtaining the image numbers having the best focus measures in the optical direction.…”

“…For this, we selected the Buddha data set, as the images in this data set contain a significant amount of noise. Depth maps obtained from the comparative methods GLV [39], MCG [40], ML [24], FMSS [41], RDF [34], and the proposed method are shown in Figure 7. From the figure, it can be observed when the depth map was extracted; every method except ours found it challenging to counter noise.…”