Identification of robust focus measure functions for the automated capturing of focused images from Ziehl-Neelsen stained sputum smear microscopy slide

Electrically Tunable Lens (ETL)-Based Variable Focus Imaging System for Parametric Surface Texture Analysis of Materials

Electrically tunable lenses (ETLs) are those with the ability to alter their optical power in response to an electric signal. This feature allows such systems to not only image the areas of interest but also obtain spatial depth perception (depth of field, DOF). The aim of the present study was to develop an ETL-based imaging system for quantitative surface analysis. Firstly, the system was calibrated to achieve high depth resolution, warranting the accurate measurement of the depth and to account for and correct any influences from external factors on the ETL. This was completed using the Tenengrad operator which effectively identified the plane of best focus as demonstrated by the linear relationship between the control current applied to the ETL and the height at which the optical system focuses. The system was then employed to measure amplitude, spatial, hybrid, and volume surface texture parameters of a model material (pharmaceutical dosage form) which were validated against the parameters obtained using a previously validated surface texture analysis technique, optical profilometry. There were no statistically significant differences between the surface texture parameters measured by the techniques, highlighting the potential application of ETL-based imaging systems as an easily adaptable and low-cost alternative surface texture analysis technique to conventional microscopy techniques.

Fast and accurate autofocus control using Gaussian standard deviation and gradient-based binning

We propose a fast and accurate autofocus algorithm using Gaussian standard deviation and gradient-based binning. Rather than iteratively searching for the optimal focus using an optimization process, the proposed algorithm directly calculates the mean of the Gaussian shaped focus measure (FM) curve to find the optimal focus location and uses the FM curve standard deviation to adapt the motion step size. The calculation only requires 3-4 defocused images to identify the center location of the FM curve. Furthermore, by assigning motion step sizes based on the FM curve standard deviation, the magnitude of the motion step is adaptively controlled according to the defocused measure, thus avoiding overshoot and unneeded image processing. Our experiment verified the proposed method is faster than the state-of-the-art Adaptive Hill-Climbing (AHC) and offers satisfactory accuracy as measured by root-mean-square error. The proposed method requires 80% fewer images for focusing compared to the AHC method. Moreover, due to this significant reduction in image processing, the proposed method reduces autofocus time to completion by 22% compared to the AHC method. Similar performance of the proposed method was observed in both well-lit and low-lighting conditions.

Detection of mycobacteria in paraffin-embedded Ziehl-Neelsen-Stained tissues using digital pathology

INTRODUCTION

Early diagnosis of paucibacillary tuberculosis represents a challenge, even with direct tissue examination. Digital pathology allows the digital analysis of tissues to identify microorganisms. We aim to develop a program to detect and quantify typical and atypical mycobacteria in paraffin-embedded Ziehl-Neelsen-stained tissues.

MATERIAL AND METHODS

Program development: The building of the program, named Pat-Scan, included pathology, systems engineering, and scientific applications. The iScan Coreo Au scanner® was used, and 9 variables were adjusted. Ten Ziehl-Neelsen-stained samples were fragmented into 2000 images and analyzed to validate the reproducibility of the bacilli images in the tissue, as detected by the software.

RESULTS

Pat-Scan included software and a scanner that were used to detect and quantify bacilli in paraffin-embedded Ziehl-Neelsen-stained tissues. All samples containing mycobacteria were successfully analyzed by the scanner, and the bacilli could be detected; these results were validated by expert pathologists by microscopy examination, and the presence of bacilli was confirmed in all cases.

CONCLUSIONS

Pat-Scan allowed the identification and quantification of mycobacteria in paraffin-embedded Ziehl-Neelsen-stained tissues, offering a reproducible diagnostic method that reduces the time for diagnosis and does not affect precision. Further validation is needed for application in the clinical setting.

Out-of-focus brain image detection in serial tissue sections

BACKGROUND

A large part of image processing workflow in brain imaging is quality control which is typically done visually. One of the most time consuming steps of the quality control process is classifying an image as in-focus or out-of-focus (OOF).

NEW METHOD

In this paper we introduce an automated way of identifying OOF brain images from serial tissue sections in large datasets (>1.5 PB). The method utilizes steerable filters (STF) to derive a focus value (FV) for each image. The FV combined with an outlier detection that applies a dynamic threshold allows for the focus classification of the images.

RESULTS

The method was tested by comparing the results of our algorithm with a visual inspection of the same images. The results support that the method works extremely well by successfully identifying OOF images within serial tissue sections with a minimal number of false positives.

COMPARISON WITH EXISTING METHODS

Our algorithm was also compared to other methods and metrics and successfully tested in different stacks of images consisting solely of simulated OOF images in order to demonstrate the applicability of the method to other large datasets.

CONCLUSIONS

We have presented a practical method to distinguish OOF images from large datasets that include serial tissue sections that can be included in an automated pre-processing image analysis pipeline.

Establishment of hybridized focus measure functions as a universal method for autofocusing

Exact focusing is essential for any automatic image capturing system. Performances of focus measure functions (FMFs) used for autofocusing are sensitive to image contents and imaging systems. Therefore, identification of universal FMF assumes a lot of significance. Eight FMFs were hybridized in pairs of two and implemented simultaneously on a single stack to calculate the hybrid focus measure. In total, 28 hybrid FMFs (HFMFs) and eight FMFs were implemented on stacks of images from three different imaging modalities. Performance of FMFs was found to the best at 50% region sampling. Accuracy, focus error, and false maxima were calculated to evaluate the performance of each FMF. Nineteen HFMFs provided >90% accuracy. Image distortion (noise, contrast, saturation, illumination, etc.) was performed to evaluate robustness of HFMFs. Hybrid of tenengrad variance and steerable filter-based (VGRnSFB) FMFs was identified as the most robust and accurate function with an accuracy of ≥90% and a relatively lower focus error and false maxima rate. Sharpness of focus curve of VGRnSFB along with eight individual FMFs was also computed to determine the efficacy of HFMF for the optimization process. VGRnSFB HFMF may be implemented for automated capturing of an image for any imaging system.

Ziehl-Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis

Ziehl-Neelsen stained microscopy is a crucial bacteriological test for tuberculosis detection, but its sensitivity is poor. According to the World Health Organization (WHO) recommendation, 300 viewfields should be analyzed to augment sensitivity, but only a few viewfields are examined due to patient load. Therefore, tuberculosis diagnosis through automated capture of the focused image (autofocusing), stitching of viewfields to form mosaics (autostitching), and automatic bacilli segmentation (grading) can significantly improve the sensitivity. However, the lack of unified datasets impedes the development of robust algorithms in these three domains. Therefore, the Ziehl-Neelsen sputum smear microscopy image database (ZNSM iDB) has been developed, and is freely available. This database contains seven categories of diverse datasets acquired from three different bright-field microscopes. Datasets related to autofocusing, autostitching, and manually segmenting bacilli can be used for developing algorithms, whereas the other four datasets are provided to streamline the sensitivity and specificity. All three categories of datasets were validated using different automated algorithms. As images available in this database have distinctive presentations with high noise and artifacts, this referral resource can also be used for the validation of robust detection algorithms. The ZNSM-iDB also assists for the development of methods in automated microscopy.