Oral cancer is a serious and growing problem in many developing and developed countries. To improve the cancer screening procedure, we developed a portable light-emitting-diode (LED)-induced autofluorescence (LIAF) imager that contains two wavelength LED excitation light sources and multiple filters to capture ex vivo oral tissue autofluorescence images. Compared with conventional means of oral cancer diagnosis, the LIAF imager is a handier, faster, and more highly reliable solution. The compact design with a tiny probe allows clinicians to easily observe autofluorescence images of hidden areas located in concave deep oral cavities. The ex vivo trials conducted in Taiwan present the design and prototype of the portable LIAF imager used for analyzing 31 patients with 221 measurement points. Using the normalized factor of normal tissues under the excitation source with 365 nm of the central wavelength and without the bandpass filter, the results revealed that the sensitivity was larger than 84%, the specificity was not smaller than over 76%, the accuracy was about 80%, and the area under curve of the receiver operating characteristic (ROC) was achieved at about 87%, respectively. The fact shows the LIAF spectroscopy has the possibilities of ex vivo diagnosis and noninvasive examinations for oral cancer.
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 ).
Currently, the cancer was examined by diagnosing the pathological changes of tumor. If the examination of cancer can diagnose the tumor before the cell occur the pathological changes, the cure rate of cancer will increase. This research develops a human-machine interface for hyper-spectral microscope. The hyper-spectral microscope can scan the specific area of cell and records the data of spectrum and intensity. These data is helpful to diagnose tumor. This study finds the hyper-spectral imaging have two higher intensity points at 550nm and 700nm, and one lower point at 640nm between the two higher points. For analyzing the hyper-spectral imaging, the intensity at the 550nm peak divided by the intensity at 700nm peak. Finally, we determine the accuracy of detection by Gaussian distribution. The accuracy of detecting normal cells achieves 89%, and the accuracy of cancer cells achieves 81%.
A new group analysis method to summarize the task-related BOLD responses based on independent
component analysis (ICA) was presented. As opposite to the previously proposed group ICA (gICA)
method, which first combined multi-subject fMRI data in either temporal or spatial domain and applied
ICA decomposition only once to the combined fMRI data to extract the task-related BOLD effects, the
method presented here applied ICA decomposition to the individual subjects' fMRI data to first find the
independent BOLD effects specifically for each individual subject. Then, the task-related independent
BOLD component was selected among the resulting independent components from the single-subject ICA
decomposition and hence grouped across subjects to derive the group inference. In this new ICA group
analysis (ICAga) method, one does not need to assume that the task-related BOLD time courses are
identical across brain areas and subjects as used in the grand ICA decomposition on the spatially
concatenated fMRI data. Neither does one need to assume that after spatial normalization, the voxels at
the same coordinates represent exactly the same functional or structural brain anatomies across different
subjects. These two assumptions have been problematic given the recent BOLD activation evidences.
Further, since the independent BOLD effects were obtained from each individual subject, the ICAga method can better account for the individual differences in the task-related BOLD effects. Unlike the gICA approach whereby the task-related BOLD effects could only be accounted for by a single unified BOLD model across multiple subjects. As a result, the newly proposed method, ICAga, was able to better fit the task-related BOLD effects at individual level and thus allow grouping more appropriate multisubject
BOLD effects in the group analysis.
KEYWORDS: Modulators, Independent component analysis, Electroencephalography, Brain, Systems modeling, Spatial filters, Signal processing, Data analysis, Digital filtering, Linear filtering
This study explores the use of Independent Component Analysis (ICA) applied to normalized logarithmic spectral
changes in the activities of brain processes separated by spatial filters learned from electroencephalogram (EEG) data
using a temporal ICA. EEG data were collected during 1-2 hour virtual-reality based driving experiments, in which
subjects were instructed to maintain their cruising position and compensate for randomly induced drifts using the
steering wheel. ICA was first applied to 30-channel EEG data to separate the recorded signals into a sum of maximally
temporally independent components (ICs) for each of 15 subjects. Logarithmic spectra of IC activities were then
submitted to PCA-ICA to find spectrally fixed and temporally independent modulator (IM) processes. The second ICA
detected and modeled independent co-modulatory systems that multiplicatively affect the activities of spatially distinct
IC processes. Across subjects, we found two consistent temporally independent modulators: theta-beta and alpha
modulators that mediate spectral activations of the distinct cortical areas when the participants experience waves of
alternating alertness and drowsiness during long hour simulated driving. Furthermore, the time courses of the theta-beta
modulator were highly correlated with concurrent changes in subject driving error (a behavioral index of drowsiness).
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