Dual-type hyperspectral microscopic imaging for the identification and analysis of intestinal fungi

A method based on a dual-type (transmission and fluorescence) hyperspectral microscopic image system was developed to identify species of intestinal fungi. Living fungi are difficult to identify via transmission spectra or fluorescence spectra alone. We propose an identification method based on both fluorescence and transmission spectra that employs a series of image processing methods. Three species of intestinal fungi were used to evaluate the method. The results demonstrate that the specificity of the model trained with dual-type spectra was 98.36%, whereas the specificities achieved by training with fluorescence spectra and transmission spectra alone were 94.04% and 92.88%, respectively.


Introduction
Intestinal fungi are important to human health, and disorders of intestinal fungi can cause many diseases. Accurate species identification is important in the study of intestinal fungi. Several methods of identifying species of intestinal fungi have been developed. The traditional methods are based on mass spectrometry (MS) technology. For example, matrixassisted laser desorption/ionization mass spectrometry (MALDI-MS) has been used for the ionization of the components of spores to identify prohibits [1] and several strains of Aspergillus species [2]. With the development of gene technology, several different polymerase chain reaction (PCR) amplifications of identifying fungi have been put forward. PCR amplification and sequencing of 18S rDNA have been used to identify yeasts and molds [3]. 16S rRNA has been used as the raw material for gene-targeted PCR to identify probiotics [4], and this combined with SYBER Green I fluorophore [5] made the species of fungi easier to identify. Although the application of these methods makes identification with high accuracy possible, all of them require destruction of the samples, which makes them unsuitable for continuous observation of samples. Fourier transform infrared spectroscopy is a nondestructive method used in fungi research, such as the analysis of four closely related species of Lactobacillus [6] and the discrimination of Lactobacillus species in breweries [7]. However, this method cannot provide morphology information about fungi and cannot identify a specific species of fungi in mixed conditions. Hyperspec information a identification, [10], and pla shows great p system was p signatures [12 [13][14][15][16]. Thes of bee pollen applications o toxigenic fun confidence le research only did not identif Because l because their fluorescent si hyperspectral types of hype [24], and the higher specifi applied to the The specificit of the imaging mixed sample 2. Dual-type  [8,9], foo ion of spectro a hyperspectra nd identificatio ging systems h issue detection oalgae [22]  were wobbled continuously by an oscillator to dissolve more oxygen in the broth. It also enabled uniform distribution of fungi, and thus the samples that were extracted from test tubes had the same density as that in the broth.
Each of the three species of fungi was used to inoculate the broth in six different test tubes, resulting in a total of 18 test tubes. We also mixed the three species of fungi together at a ratio of 1:1:1 in one test tube. The experiment was conducted over a period of 48 h, which we considered to begin (0 h) after a 12-h period of cultivation. We extracted a sample from each of the 18 single-species test tubes at the beginning of the experiment, and we extracted a sample from the test tube of mixed fungi at the beginning of the experiment and after 24 and 48 h.

Transmission spectrum and fluorescence spectrum
Obtaining transmission spectral images only required turning on the halogen lamp. When the broadband-emitted light passed through the sample, a portion of it was absorbed by the sample and the rest carried the transmission information of the sample and was received by the CMOS.
Based on the spectral images we obtained, the transmittance of a certain pixel at a certain wavelength was defined as follows: Here, T(λ) represents the transmittance of the pixel at a wavelength of λ. Because the grayscale of the pixel is proportional to the light intensity, I(λ) sample and I(λ) background represent the intensity of the pixel with and without the sample, respectively.
To obtain the fluorescence spectra, it was necessary to turn on the xenon lamp with a narrow band-pass filter at 361 nm and switch off the halogen lamp. This short-wavelength radiation was easily able to excite the fluorescence of the fungus. The fluorescence passed through the dichroic mirror and was detected by CMOS, yielding the fluorescence spectra of the fungus. The fluorescence intensity was normalized as follows before the analysis: Here, I n (λ) represents the normalized intensity of the pixel at the emission wavelength of λ, I(λ) is the intensity of the pixel at the emission wavelength of λ, and I λ-max is the highest intensity of the pixel over the whole wavelength range.
The transmission and fluorescence spectral images of the three species of fungi were obtained in the spectral range from 460 nm to 660 nm at increments of 2 nm. The exposure time of each transmission and fluorescence spectral image was 0.03 seconds and 10 seconds respectively. Examples of different single-wavelength images are shown in Fig. 3. We randomly selected 100 pixels from each of the standard samples (three single species of fungi). Their average transmittance curves and normalized fluorescence intensity curves are shown in Figs. 3(a) and (b), respectively. Candida utilis can easily be distinguished from the other two species of fungi by the fluorescence characteristic peaks, whereas the fluorescence spectra of the Aspergillus flavus and Aspergillus fumigatus are too similar to be distinguished from each other. However, their transmission spectra curves are clearly different. Thus, the dual-type spectra were used for identification.
Because the signal-to-noise ratios of the transmission spectral images ranging from 460 nm to 558 nm were too low, only the transmission images ranging from 560 nm to 660 nm were used in the classification. Hence, the transmission spectra ranging from 560 nm to 660 nm (a total of 51 bands), together with the fluorescence spectra from 460 nm to 660 nm (a total of 101bands), constituted a dual-type spectral data set for use in identification in our study.

Results a
We obtained images conta images. For e obtained. Thi in Table 1       The results show that the SPEC of Candida utilis and Aspergillus flavus according to the three models were all 100%. However, the SPEC of Aspergillus fumigatus according to the first model was 94.04%, that according to the second model was 92.88%, and that according to the last model was up to 98.36%. The SEN of Candida utilis and Aspergillus fumigatus according to the three models were also 100%, but the SEN of Aspergillus flavus according to the first model was 93.67%, that according to the second model was 92.33%, and that according to the last model was 98.33%. This shows that the model trained using both fluorescence and transmission spectral data had a higher sensitivity and specificity than the model trained using only fluorescence spectral data or transmission spectral data. Therefore, we chose the dual-type spectral data to classify the fungi in the mixed sample.
Compared with the Fourier transform infrared spectroscopy method for fungi identification, whose average specificity was 91.5% [25], our method had a higher specificity of up to 98%. To take full advantage of the hyperspectral imaging technology and enhance the visualization quality, a series of image processing methods was designed and added to the identification process.
The identification process for the mixed sample was as follows. The first step was to obtain the spatial information of the fungi pixels so as to release the calculation burden. If the spectra data of all the pixels in the image was used for identification, the calculation would be very large which contains more than 39 million of data. Therefore, to obtain the spatial information of the fungi pixels, the grayscale image of the mixed sample was converted to a binary image using a certain threshold. Pixels with intensities beyond the threshold were set to 65535; otherwise they were set to 0. As Fig. 4 shows, both the fluorescence image at 546 nm ( Fig. 4(a) converted to respectively. satisfactory; t the fluorescen transmission metabolic ma If only one th have been too to-noise ratio method was u was 512 × 512  ferent grayscale grayscale thres suring that the in Fig. 6(b). C the improvem ormation ensur abolites of the f ied. As Fig. 6 When the the circular pa block in the b fungi ( Fig. 7 classifier, wh Spatial inform precise spatia transmission i then labeled (Fig. 7(f)). F corresponding grayscale hist used to remov region in blue We calculated whole region without the respectively. object area w arts at the uppe broth, whose sp (b)). All of th hich was traine mation for the f al information information (F with different For the purpo g pseudo colo togram (Fig. 7  Based on the method using dual-type spectral information (Fig. 8), the growth competition in the mixed sample could be observed. Because the method was nondestructive and fast (it took 17 min), we were able to reserve the fungi repeatedly or continuously. As Fig. 9 shows, the growth competition of the fungi at different times (0, 24, and 48 h), the distribution of the fungi, and the morphology information of the three species of fungi could be observed intuitively, which proved that our system makes continuous observation of mixed samples possible. According to the results, we could see that the growth trends of three species of fungi were different. The Candida Utilis and Aspergillus Flavus both soon disappeared since the growth of them was inhibited, while Aspergillus Fumigatus dominated in the mixed condition, which indicated that they do exist competition in the mixed samples.

Conclusions
It has been verified in this study that the application of dual-type spectrum technology to the identification of fungi yields higher specificity (98.03%) than the single-type spectrum technology (94.04% for fluorescence spectra and 92.88% for transmission spectra). The image segmentation method for binary image acquisition ensured more rigorous results, and the connected-region labeling algorithm improved the specificity of identification. This method is very effective in the identification of fungi in mixed samples. We believe that this fast, nondestructive method has great potential for use in studying fungi and will contribute to further research and development in the identification of biological samples.