The eye, the photoreceptive organ used to perceive the external environment, is of great importance to humans. It has been proven that some diseases in humans are accompanied by fundus changes; therefore, the health status of people may be interpreted from retinal images. However, the human eye is not a perfect refractive system for the existence of ocular aberrations. These aberrations not only affect the ability of human visual discrimination and recognition, but restrict the observation of the fine structures of human eye and reduce the possibility of exploring the mechanisms of eye disease. Adaptive optics (AO) is a technique that corrects optical wavefront aberrations. Once integrated into ophthalmoscopes, AO enables retinal imaging at the cellular level. This paper illustrates the principle of AO in correcting wavefront aberrations in human eyes, and then reviews the applications and advances of AO in ophthalmology, including the adaptive optics fundus camera (AO-FC), the adaptive optics scanning laser ophthalmoscope (AO-SLO), the adaptive optics optical coherence tomography (AO-OCT), and their combined multimodal imaging technologies. The future development trend of AO in ophthalmology is also prospected.
Fluorescence imaging is an important and efficient tool in cell biology and biomedical research. In order to observe the dynamics of biological macromolecules such as DNA, RNA and proteins in live cells, it is extremely necessary to surpass the Abbe diffraction limit in microscopic imaging. Single-molecule localization microscopy (SMLM) is a sort of super-resolution imaging technique that can obtain a large number of images of sparse fluorescent molecules by the use of photoswitchable fluorescent probes and single-molecule localization technology. The center positions of fluorescent molecules in the images are precisely located, and then the entire sample pattern is reconstructed with super resolution. In this paper, we present a single-molecule localization algorithm (SMLA) that is based on blind deconvolution and centroid localization (BDCL) method. Single-molecule localization and image reconstruction of 15,000/9990 frames of original images of tubulins are accomplished. In addition, this fluorophore localization algorithm is used to localize high particle-density images. The results show that our BDCL-SMLA method is a reasonable attempt and useful method for SMLM imaging when the imaging system is unknown.
Hyperspectral microscopic imaging (HMI) technology is a non-contact optical diagnostic method, which combines hyperspectral imaging (HSI) technology with microscopy to provide both spectral information and image information of the samples to be measured. In this paper, basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM) were classified based on synthetic RGB image data from HMI cube by using four classification methods extreme learning machine (ELM), support vector machine (SVM), decision tree and random forest (RF). The highest classification accuracy of 0.791±0.060 and a KAPPA value of 0.685±0.095 were obtained when color moment, gray level co-occurrence matrix (GLCM) and local binary pattern (LBP) were used for image feature extraction, feature dimensions were reduced by the PLS, the sample sets were divided by the hold-out method, and the tissues were classified by the SVM model.
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