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Automated Spore Measurements Using Microscopy, Image Analysis, and Peak Recognition of NearMonodisperse Aerosols a
Jeff Wagner & Janet Macher
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California Department of Public Health, Environmental Health Laboratory Branch, Richmond, California, USA Available online: 14 Mar 2012
To cite this article: Jeff Wagner & Janet Macher (2012): Automated Spore Measurements Using Microscopy, Image Analysis, and Peak Recognition of Near-Monodisperse Aerosols, Aerosol Science and Technology, 46:8, 862-873 To link to this article: http://dx.doi.org/10.1080/02786826.2012.674232
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Aerosol Science and Technology, 46:862–873, 2012 C American Association for Aerosol Research Copyright ISSN: 0278-6826 print / 1521-7388 online DOI: 10.1080/02786826.2012.674232
Automated Spore Measurements Using Microscopy, Image Analysis, and Peak Recognition of Near-Monodisperse Aerosols Jeff Wagner and Janet Macher
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Environmental Health Laboratory Branch, California Department of Public Health, Richmond, California, USA
Rapid detection of airborne fungal and bacterial spores would enable public agencies to respond quickly and appropriately to intentional releases of hazardous aerosols. Automated analysis of microscope images and automated detection of near-monodisperse peaks in aerosol size distribution data offer complementary approaches to traditional methods for the identification and counting of fungal and bacterial spores. First, spores of the fungus Scopulariopsis brevicaulis were aerosolized in a chamber and then collected with a slit impactor; later, digital microscope images were analyzed manually to determine spore cluster distributions. The images also were analyzed with ImageJ, a program that automatically outlined objects and measured Feret’s diameter, area, perimeter, and circularity. These characteristics were used to identify spore clusters automatically using two data analysis methods. Second, a computer program was developed to discriminate near-monodisperse bioaerosol peaks from those for polydisperse ambient particulate matter (PM) and was successfully tested using simulated and real aerosol mixtures. The observed agreement between manual and automated spore counts and the ability to detect spore peaks suggest that it may be possible to develop a system to recognize intentional releases rapidly through examination of particle morphology and size distributions. The peak detection procedure is potentially the fastest technique when used with real-time instrument data, but assumes that intentional releases would consist of large numbers of uniformly sized particles in the respirable size range.
[Supplementary materials are available for this article. Go to the publisher’s online edition of Aerosol Science and Technology to view the free supplementary files.] 1. INTRODUCTION Fungi and bacteria constitute ubiquitous indoor and outdoor presences and play significant roles in the natural environment and our daily lives. The release of numerous spores into the air Received 29 August 2011; accepted 13 February 2012. The authors thank student interns Shweta Teckchandani, Kaveh Hemati, and Priscillia Tanbun of the University of California, Berkeley. Address correspondence to Jeff Wagner, Environmental Health Laboratory Branch (EHLB), California Department of Public Health, 850 Marina Bay Parkway, Richmond, CA 94804, USA. E-mail:
[email protected]
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is a mechanism by which these organisms disperse over wide regions. For example, Elbert et al. (2007) used air sample data and budget calculations to estimate the global average emission rate of basidiospores (∼17 Tg/yr) and total fungal spores (∼50 Tg/yr). These rates can be compared to estimates of global emissions of ∼47 Tg/yr for anthropogenic primary organic aerosols (Volkamer et al. 2006) and 12–70 Tg/yr for secondary organic aerosols (Kanakidou et al. 2005). Easy and accurate identification of fungal and bacterial spores is relevant to many aspects of human health and comfort as well as to plant, animal, and microbial ecology. Researchers collect airborne microorganisms using a variety of methods (e.g., impaction, impingement into liquid, and filtration) and analyze air samples by microscopy, culture, biological or chemical assays, and genetic detection. Light microscopy, a widely used analytical technique, is compatible with a number of air sample formats and requires minimal sample preparation. However, this procedure can be time-consuming, analysts require extensive training and experience, and humans are prone to error. Therefore, automated methods that combine minimal sample handling with rapid and accurate spore characterization have the potential to provide cost-effective and rapid alternatives, if demonstrated to be accurate and reliable, and could have broad relevance. The rapid detection of hazardous aerosols would enable public agencies to respond quickly and appropriately to intentional releases of infectious agents, biological toxins, or nonvolatile chemical, mineral, or radioactive particles. Unfortunately, most air monitoring programs rely on collection of 24-h filter samples, which provide notification at a considerable delay and do not identify the exact time or peak concentration of a release. However, information on the time and amount of a release is necessary for accurate identification of the most highly exposed persons so that they receive priority treatment. Monitors that provide near-real-time notification are used in some high-risk facilities (e.g., the US Postal Service’s Biohazard Detection System) but only for a limited number of threat agents (e.g., the bacterium Bacillus anthracis, the cause of anthrax) (O’Neill 2003).
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AUTOMATED SPORE MEASUREMENTS
Particle collection with a slit impactor allows for shorter sampling times relative to a filter, typically less than 1 h. Slide samples traditionally are analyzed using manual microscopy, which is time-consuming and dependent on a human analyst’s ability to distinguish particles and accurately count and size them, which may vary within and between analysts. Automated extraction of information from pictures, i.e., image analysis, is an alternative to manual examination that may be more accurate and faster if sequential samples could be fed directly into an image acquisition and data analysis device that ideally could be deployed in the field and operated remotely. However, an automated image analysis system may incorrectly classify some objects as particles if the objects meet the program’s recognition criteria, a disadvantage relative to a human analyst. Nevertheless, computer results would be more reproducible than manual analyses and would not suffer from subjective bias or random errors (Ruusuvuori et al. 2008). Real-time particle counting with data averaging times of minutes to hours has the potential to be the fastest detection method if algorithms could be developed to recognize uniformly sized particles within a population of ambient particulate matter (PM). Particles within monodisperse peaks subsequently could be examined to determine the nature of the material, e.g., by Raman micro-spectroscopy, which is rapid and nondestructive, provided that a library contains a spectral signature for the unknown material. We previously have determined that Raman micro-spectroscopy can identify single fungal spores to the genus level and can differentiate between spores with similar morphology (Macher et al. 2011). This pilot project utilized impactor slides previously collected in a small chamber and outdoors. In the first part of the project, the chamber samples were used to evaluate the comparability of three methods to count Scopulariopsis brevicaulis spores: (i) manual counting (the reference method), (ii) image analysis plus morphological criteria, and (iii) image analysis plus a projected-area-based equation. In the second part of the project, a computer program was developed to detect the presence of nearly monodisperse particle modes in particle size distribution data from any technique, including real-time particle instruments. The feasibility of this approach was evaluated using simulated and real data.
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MATERIALS AND METHODS
2.1. Sample Collection 2.1.1. S. brevicaulis Chamber Samples The generation of S. brevicaulis aerosols inside a glove box with a venturi dry powder disperser (In-Tox Products, Albuquerque, NM) and collection of slit impactor samples (Air-OCell, Zefon International Inc., Ocala, FL) have been described previously: count median spore diameter (CMD), 6.9 µm; geometric standard deviation (GSD), 1.2 (Macher et al. 2008). The approximately 1-mm × 14-mm deposits on the five 15min slides from this study were analyzed by three methods, i.e.,
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manual spore count, morphological spore recognition criteria, and estimation of the number of spores from projected area and known spore diameter (Section 2.2). 2.1.2. Outdoor Air Sample for Automated Detection of Near-Monodisperse Peaks An outdoor air sample containing ambient PM (including naturally generated, biological particles, e.g., pollen grains and fungal spores) was obtained from a study in an agricultural area of central California that employed a time-resolved, 24-h slit impactor (Seven-Day Recording Volumetric Spore Trap; Burkard Manufacturing Co. Ltd., Rickmansworth, UK) (slit dimensions, 2 mm × 14 mm; particle deposition area, approximately 14 mm × 48 mm) (Harley et al. 2009). A slide was chosen that exhibited relatively high numbers of fungal spores, in particular, Aspergillus/Penicillium species, Cladosporium species, and smuts (163, 1701, and 91 spores, respectively) (Section 2.3). 2.2. S. brevicaulis Spore Counts 2.2.1. Image Analysis of S. brevicaulis Samples Images were acquired at 200× magnification with a transmitted light microscope and camera (Nikon Eclipse E400, Melville, NY, USA) for five Air-O-Cell slides. High-contrast, near-binary images of S. brevicaulis spores and of the background were obtained using plane-polarized light, an NCB11 filter, and the condenser aperture stopped all the way down (Figure 1a). Internal spore structures were minimized by deliberately defocusing the microscope. The digital images were stored as 2088-pixel × 1550-pixel, 24-bit, color JPEG (Joint Photographic Experts Group) files. Size measurements for the given magnification and imaging settings were converted from pixels to micrometer using a National Physical Laboratory (UK)-calibrated stage micrometer (Pyser SGI Ltd., Kent, UK) (pixel diameter: 0.17 µm). Each slide analysis utilized a 5 × 7 matrix of 35 equally spaced images (camera fields of view) covering the 1-mm × 14-mm particle deposit, for a total of 175 images: five slides, 35 images per slide identified by width position A–E (images ∼0.17 mm apart) and length position 1–7 (images ∼1.8 mm apart) (Figure S1; supplementary materials available online). Particle density was highest along the centers of the deposits. Therefore, the number of spores per image varied, ranging by manual count from 0 to 79 single spores, 0 to 19 doubles, and 0 to 11 larger clusters per image with medians of 14, 1, and 0 spores per image, respectively (Table S1). Objects in the digital images were numbered and their dimensions were measured with image-processing and analysis software in Java format (ImageJ, Version 1.37, National Institutes of Health, Bethesda, MD: http://rsb.info.nih.gov/ij/). ImageJ is a public domain image-processing program that can display, edit, analyze, process, save, and print 8-, 16-, and 32-bit images. The program can read many image formats (e.g., TIFF, PNG, GIF, JPEG, and BMP) and can be customized to analyze and process images in a user-specified manner.
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are increasingly elongated polygons, and 1 indicates a perfect circle. The entire image acquisition and analysis procedure took approximately 1 h per sample. Based on previous modifications to other microscopes in our laboratory (Wagner and Macher 2003), the addition of an automated stage for image acquisition and processing of images with ImageJ macros should reduce this processing time by a factor of 2–3, although neither was implemented in this pilot project.
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2.2.2. Manual Counting of S. brevicaulis Spores Three persons independently examined 16 images and compared their results to develop consistent manual counting criteria. Images from all five Air-O-Cell slides were included. Images were chosen to have sufficient S. brevicaulis spores for meaningful comparisons, i.e., neither too sparse nor overloaded (2–39 single spores per image), as well as a range of particle arrangements so that the counting rules would address all anticipated spore configurations. Using the following rules, one trained counter then examined all 175 images, recorded the number of single spores and clusters, repeated these measurements, and resolved disagreements between the two data sets to produce the final manual count data. Counting criteria: • Outline (particle borders) • Count only spores with complete outlines, i.e., without gaps or flaws. • Edges (spores touching the edges of the images) • Do not count spores if their outlines touch an edge. • Clusters • Count spores as clusters if the outlines of the individual spores touch each other. • Do not count clusters of ≥5 spores. FIG. 1. (a) Original and (b) ImageJ-processed images of (i) debris: particles 34 and 35 (lower right-hand corner); (ii) single S. brevicaulis spores: particles 13, 14, 19, 24, 25, 29, 30, 33, and 36; and (iii) a double S. brevicaulis spore: particle 20. Each image is 140-µm wide.
First, the color photographs were converted to binary images by separating the darker particles from the brighter background using an intensity threshold value. This value typically was constant for a given sample and was set manually at the beginning of an analysis. The “Analyze Particles” command then counted and measured all objects in the images. This command works by scanning an image until the edge of an object is encountered, which is then outlined (Figure 1b). Feret’s diameter (dF , the longest distance between any two points along the boundary of an object), area (A, projected area), perimeter (P), and circularity (C, 4π × A × P2) were recorded for each particle. Values of C range from 0 to 1, where values approaching zero
2.2.3. Automated ImageJ Data Analysis to Identify S. brevicaulis Spores and Spore Clusters 2.2.3.1. Morphological criteria: training spores. Summary statistics, acquired from a set of manually identified, “training spores,” were used to determine morphological recognition criteria for S. brevicaulis spores (for single spores: 11 images from two slides were included; for double spores: 20 images from two slides; and for ≥3 spores: 21 images from four slides; Table S2). Criteria values were identified that correctly recognized the greatest numbers of spores and distinguished clusters from each other and from debris and background. Objects could be distinguished from debris and recognized as single or double S. brevicaulis spores if dF and A were between the 1st and 99th percentiles and C exceeded the 1st percentile of the respective distributions (Tables S2 and S3). An upper/lower dF cutpoint of 9.5 µm was used to avoid counting a particle as both a singlet and a doublet because
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the respective 99th and 1st percentiles were similar (9.6 and 9.5 µm) (Tables S2 and S3). Inclusion of a criterion for P did not improve distinction between doublets and singlets. Three S. brevicaulis spores could be configured in a straight chain (with dF approximately three times the single spore diameter), in a triangle (with dF approximately two times the single spore diameter), or somewhere in between. Therefore, circularity was not informative for identification of clusters larger than two spores. Criteria for dF , A, and P for clusters of ≥3 spores were selected to exclude all known doublets. Particles that exceeded any or all of the 34th, 31st, and 21st percentiles for dF , A, and P, respectively, were counted as ≥3 spores (Tables S2 and S3). The morphological criteria were applied to the ImageJ data for the training spores, and objects that the criteria identified as S. brevicaulis spores were compared manually with the highcontrast photographs to determine how accurately the criteria recognized known spores. 2.2.3.2. Morphological criteria: confirmation particles. The morphological criteria derived from the training spores were applied to the ImageJ data for particles in additional images to determine how correctly the criteria recognized S. brevicaulis spores (for single spores: five images from one slide were included; double spores: 24 images from one slide; ≥3 spores: 20 images from two slides; Table S4). Objects that the morphological criteria identified as S. brevicaulis spores in these confirmatory images were compared manually with the high-contrast photographs to identify spores that were identified correctly and those that were misclassified. 2.2.4. Automated Estimation of Spore Number Using Known Diameter and Observed Area The number of S. brevicaulis spores in each particle (N) also was estimated from Equation (1), which has been used to recover three-dimensional particle properties from twodimensional projected images (Fang et al. 1998; Hu et al. 2003; Hu and Koylu 2004). The constant 1.15 and exponent 1.09 approximately account for particle overlap. N = 1.15
A π d¯2 /4
1.09 .
[1]
F
The parameter d¯F is the average, single-spore Feret diameter. For S. brevicaulis, d¯F was determined previously using an optical particle counter (Macher et al. 2008) and was confirmed for the training spores to be 6.9 µm (Table S2). The ability of Equation (1) to correctly estimate spore number was determined by application to 445, 192, 56, and 21 known single, double, triple, and quadruple spores, respectively. Nonoverlapping cutpoints for S. brevicaulis spore clusters were selected from the mean and two standard deviations (SDs) for known single spores and the mean and one SD for known two-, three-, and four-spore clusters: ≥0.4 –
