Abstract:We present a novel embedded relay lens hyperspectral imaging system (ERL-HIS) with high spectral resolution (nominal spectral resolution of 2.8 nm) and spatial resolution (30 μm×8 μm) that transfers the scanning plane to an additional imaging plane through the internal relay lens so as to alleviate all outside moving parts for the scanning mechanism used in the traditional HIS, where image scanning is achieved by the relative movement between the object and hyperspectrometer. The ERL-HIS also enables high-spee… Show more
“…A microscope-based HSI system (for more details, see Hsieh et al 13 ) was used to produce the HSI data of thin, frozen sections cut from excised oral cancer specimens. Briefly, a regular inverted fluorescence microscope was connected to a customdesigned and manufactured embedded relay-lens system capable of decomposing the two-dimensional (2-D) image of a thin-sliced object, one line at a time, into continuous spectra.…”
“…The latter calibration image was used to remove the nonuniformity caused by uneven illumination, periodic scan line stripping, and the reflection from the glass medium. 11,13 After data preprocessing, the HSI data cube was first reconstructed into a series of 2-D image slices, each representing a 2-D autofluorescence intensity of the thin tissue slice at an emission wavelength. The two image dimensions corresponded to the spatial x-and y-axis of the 2-D object slice.…”
Section: Data Preprocessing and Image Reconstructionmentioning
confidence: 99%
“…The scanning of the image plan can be achieved using a relatively low-cost moving platform to move the relay-lens system with less precision because the scanned image plan has first been magnified through the projection optical path. 13 However, the acquisition from hyperspectral images may reflect a complex mixture of different sources. For example, the mixture can come from the underlying reorganization of biochemical fluorophores in and around a tumorigenesis area.…”
Abstract. Recently, hyperspectral imaging (HSI) systems, which can provide 100 or more wavelengths of emission autofluorescence measures, have been used to delineate more complete spectral patterns associated with certain molecules relevant to cancerization. Such a spectral fingerprint may reliably correspond to a certain type of molecule and thus can be treated as a biomarker for the presence of that molecule. However, the outcomes of HSI systems can be a complex mixture of characteristic spectra of a variety of molecules as well as optical interferences due to reflection, scattering, and refraction. As a result, the mixed nature of raw HSI data might obscure the extraction of consistent spectral fingerprints. Here we present the extraction of the characteristic spectra associated with keratinized tissues from the HSI data of tissue sections from 30 oral cancer patients (31 tissue samples in total), excited at two different wavelength ranges (330 to 385 and 470 to 490 nm), using independent and principal component analysis (ICA and PCA) methods. The results showed that for both excitation wavelength ranges, ICA was able to resolve much more reliable spectral fingerprints associated with the keratinized tissues for all the oral cancer tissue sections with significantly higher mean correlation coefficients as compared to PCA (p < 0.001).
“…A microscope-based HSI system (for more details, see Hsieh et al 13 ) was used to produce the HSI data of thin, frozen sections cut from excised oral cancer specimens. Briefly, a regular inverted fluorescence microscope was connected to a customdesigned and manufactured embedded relay-lens system capable of decomposing the two-dimensional (2-D) image of a thin-sliced object, one line at a time, into continuous spectra.…”
“…The latter calibration image was used to remove the nonuniformity caused by uneven illumination, periodic scan line stripping, and the reflection from the glass medium. 11,13 After data preprocessing, the HSI data cube was first reconstructed into a series of 2-D image slices, each representing a 2-D autofluorescence intensity of the thin tissue slice at an emission wavelength. The two image dimensions corresponded to the spatial x-and y-axis of the 2-D object slice.…”
Section: Data Preprocessing and Image Reconstructionmentioning
confidence: 99%
“…The scanning of the image plan can be achieved using a relatively low-cost moving platform to move the relay-lens system with less precision because the scanned image plan has first been magnified through the projection optical path. 13 However, the acquisition from hyperspectral images may reflect a complex mixture of different sources. For example, the mixture can come from the underlying reorganization of biochemical fluorophores in and around a tumorigenesis area.…”
Abstract. Recently, hyperspectral imaging (HSI) systems, which can provide 100 or more wavelengths of emission autofluorescence measures, have been used to delineate more complete spectral patterns associated with certain molecules relevant to cancerization. Such a spectral fingerprint may reliably correspond to a certain type of molecule and thus can be treated as a biomarker for the presence of that molecule. However, the outcomes of HSI systems can be a complex mixture of characteristic spectra of a variety of molecules as well as optical interferences due to reflection, scattering, and refraction. As a result, the mixed nature of raw HSI data might obscure the extraction of consistent spectral fingerprints. Here we present the extraction of the characteristic spectra associated with keratinized tissues from the HSI data of tissue sections from 30 oral cancer patients (31 tissue samples in total), excited at two different wavelength ranges (330 to 385 and 470 to 490 nm), using independent and principal component analysis (ICA and PCA) methods. The results showed that for both excitation wavelength ranges, ICA was able to resolve much more reliable spectral fingerprints associated with the keratinized tissues for all the oral cancer tissue sections with significantly higher mean correlation coefficients as compared to PCA (p < 0.001).
Cytopathological examination through biopsy is very important for carcinoma detection. The embedded relay lens microscopic hyperspectral imaging system (ERL-MHIS) provides a morphological image of a biopsy sample and the spectrum of each pixel in the image simultaneously. Based on the ERL-MHIS, this work develops morphological and spectral methods to diagnose oral carcinoma biopsy. In morphological discrimination, the fractal dimension method is applied to differentiate between normal and abnormal tissues. In spectral identification, normal and cancerous cells are distinguished using five methods. However, the spectra of normal and cancerous cells vary with patient. The diagnostic performances of the five methods are thus not ideal. Hence, the proposed cocktail approach is used to determine the effectiveness of the spectral methods in correlating with the sampling conditions. And then we use a combination of effective spectral methods according to the sample conditions for diagnosing a sample. A total of 68 biopsies from 34 patients are analyzed using the ERL-MHIS. The results demonstrate a sensitivity of 90 ± 4.53 % and a specificity of 87.8 ± 5.21 %. Furthermore, in our survey, this system is the first time utilized to study oral carcinoma biopsies.
“…The distortion would affect the morphological information of the image. Hence, this paper proposes a novel embedded relay lens microscopic hyperspectral imaging (ERL-MHSI) system that used our previous design to be the scanning part [20]. The demonstrated diagrams of the proposed system are showed in Figure 1(b).…”
This paper develops a novel embedded relay lens microscopic hyperspectral imaging system (ERL-MHSI) with high spectral resolution (nominal spectral resolution of 2.8 nm) and spatial resolution (30 μm × 10 μm) for cancer diagnosis. The ERL-MHSI system has transmittance and fluorescence mode. The transmittance can provide the morphological information for pathological diagnosis, and the fluorescence of cells or tissue can provide the characteristic signature for identification of normal and abnormal. In this work, the development of the ERL-MHSI system is discussed and the capability of the system is demonstrated by diagnosing early stage oral cancer of twenty mice in vitro. The best sensitivity for identifying normal cells and squamous cell carcinoma (SCC) was 100%. The best specificity for identifying normal cells and SCC was 99%. The best sensitivity for identifying normal cells and dysplasia was 99%. The best specificity for identifying normal cells and dysplasia was 97%. This work also utilizes fractal dimension to analyze the morphological information and find the significant different values between normal and SCC.
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