In this study, we developed a digital shade-matching device for dental color determination using the support vector machine (SVM) algorithm. Shade-matching was performed using shade tabs. For the hardware, the typically used intraoral camera was modified to apply the cross-polarization scheme and block the light from outside, which can lead to shade-matching errors. For reliable experiments, a precise robot arm with ±0.1 mm position repeatability and a specially designed jig to fix the position of the VITA 3D-master (3D) shade tabs were used. For consistent color performance, color calibration was performed with five standard colors having color values as the mean color values of the five shade tabs of the 3D. By using the SVM algorithm, hyperplanes and support vectors for 3D shade tabs were obtained with a database organized using five developed devices. Subsequently, shade matching was performed by measuring 3D shade tabs, as opposed to real teeth, with three additional devices. On average, more than 90% matching accuracy and a less than 1% failure rate were achieved with all devices for 10 measurements. In addition, we compared the classification algorithm with other classification algorithms, such as logistic regression, random forest, and k-nearest neighbors, using the leave-pair-out cross-validation method to verify the classification performance of the SVM algorithm. Our proposed scheme can be an optimum solution for the quantitative measurement of tooth color with high accuracy.

A biopsy is often performed for the diagnosis of cancer during a surgical operation. In addition, pathological biopsy is required to discriminate the margin between cancer tissues and normal tissues in surgical specimens. In this study, we presented a novel method for discriminating between tumor and normal tissues using fluorescence lifetime endoscopy (FLE). We demonstrated the relationship between the fluorescence lifetime and pH in fluorescein using the proposed fluorescence lifetime measurement system. We also showed that cancer could be diagnosed based on this relationship by assessing differences in pH based fluorescence lifetime between cancer and normal tissues using two different types of tumor such as breast tumors (MDA-MB-361) and skin tumors (A375), where cancer tissues have ranged in pH from 4.5 to 7.0 and normal tissues have ranged in pH from 7.0 to 7.4. To support this approach, we performed hematoxylin and eosin (H&E) staining test of normal and cancer tissues within a certain area. From these results, we showed the ability to diagnose a cancer using FLE technique, which were consistent with the diagnosis of a cancer with H&E staining test. In summary, the proposed pH-based FLE technique could provide a real time, in vivo, and in-situ clinical diagnostic method for the cancer surgical and could be presented as an alternative to biopsy procedures.

We demonstrated GPU accelerated real-time confocal fluorescence lifetime imaging microscopy (FLIM) based on the analog mean-delay (AMD) method. Our algorithm was verified for various fluorescence lifetimes and photon numbers. The GPU processing time was faster than the physical scanning time for images up to 800 × 800, and more than 149 times faster than a single core CPU. The frame rate of our system was demonstrated to be 13 fps for a 200 × 200 pixel image when observing maize vascular tissue. This system can be utilized for observing dynamic biological reactions, medical diagnosis, and real-time industrial inspection.

Goblet cells (GCs) in the intestine are specialized epithelial cells that secrete mucins to form the protective mucous layer. GCs are important in maintaining intestinal homeostasis, and the alteration of GCs is observed in inflammatory bowel diseases (IBDs) and neoplastic lesions. In the Barrett’s esophagus, the presence of GCs is used as a marker of specialized intestinal metaplasia. Various endomicroscopic imaging methods have been used for imaging intestinal GCs, but high-speed and high-contrast GC imaging has been still difficult. In this study, we developed a high-contrast endoscopic GC imaging method: fluorescence endomicroscopy using moxifloxacin as a GC labeling agent. Moxifloxacin based fluorescence imaging of GCs was verified by using two-photon microscopy (TPM) in the normal mouse colon. Label-free TPM, which could visualize GCs in a negative contrast, was used as the reference. High-speed GC imaging was demonstrated by using confocal microscopy and endomicroscopy in the normal mouse colon. Confocal microscopy was applied to dextran sulfate sodium (DSS) induced colitis mouse models for the detection of GC depletion. Moxifloxacin based GC imaging was demonstrated not only by 3D microscopies but also by wide-field fluorescence microscopy, and intestinal GCs in the superficial region were imaged. Moxifloxacin based endomicroscopy has a potential for the application to human subjects by using FDA approved moxifloxacin.

The range of imaging depth in optical resolution photoacoustic microscopy (PAM) is limited by the short depth of focus of high-numerical aperture objective lenses. In this paper, focus tunable lens modulation has been employed at the resonant frequency of focus tunable lenses in order to enhance both the range of imaging depth and the scanning speed. By electrically controlling the focal length in the axial direction of the sample, the range of imaging depth was extended approximately 1.22 times and the scanning speed was enhanced by approximately 7.40 times, in comparison to corresponding values of conventional PAM systems.