Spectral Confocal Displacement Sensor is a new type of displacement sensor that can meet the requirements of high precision, high speed and non-destructive testing, and can be applied in aerospace, medical and industrial testing fields. The traditional point-scan spectral confocal displacement sensor only collects depth information of one point at a time, which is slow and has a large amount of back-end data. In order to improve the scanning speed of the point-scan spectral confocal displacement sensor and reduce the workload of data processing, it is proposed to extend the point-scan type to line-scan type, which can obtain the depth information of thousands of points at one time, improve the scanning speed and reduce the workload of data processing by processing all the points on the scanning line at one time. Aiming at the dispersive objective lens design in the line-scan spectral confocal displacement sensor, a dispersive lens with a large linear dispersion range is designed based on the principle of linear axial chromatic aberration design. The lens consists of four single lenses and two double-glued lenses and a beam-splitting prism, in order to solve the problem of energy uniformity into the spectrometer and the problem of uniformity of illumination on the image surface, the double telecentric optical path design is adopted, and the size of the detector element selected is 5.5 um, and the Nyquist cutoff frequency is 91 Lp/mm. The design results show that: the dispersive range of the lens reaches 3 mm, and the length of the scanning line reaches 10 mm. At the Nyquist cutoff frequency, the MTF value of the whole field of view is greater than 0.6, which indicates that the system has good imaging quality.
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.
Chest X-ray is the commonly used method to diagnose pneumonia. How to correctly interpret the image information is always the main challenge faced by doctors. Convolution Neural Network (CNN) is a popular deep learning algorithm with excellent image recognition performance, and has been used widely in automatic recognition and diagnosis of medical images. This paper studies the classification of normal and pneumonia with more than 5000 chest X-ray images by employing three CNN models of VGG16, VGG19 and Inception_V3. The performances of each model for classification was evaluated and compared.
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