In this paper, we present a new non-contact strategy to estimate the Peripheral Oxygen Saturation (SPO2) based on the Eulerian motion video magnification technique and a signal processing technique, The magnification procedure was carried out using two approaches : the Hermite decomposition and the Gaussian decomposition. The SpO2 is estimated from the signals extracted after magnification process using the red and the blue Chanel of the frame. We have tested the method on five healthy subjects using videos obtained from the google-meet video conference platform. To compare the performance of the methods, we compute the mean average error and metrics issues from the Bland and Altman analysis to investigate the agreement of the methods with respect to a contact pulse oximeter device as reference. The proposed solution shows an agreement with respect to the reference of most of 98%
In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and an Artificial Hydrocarbon Networks (AHN) as classifier. After the magnification procedure, a AHN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. The respiratory rate (RR) is estimated from the classified frames. We have tested the method on 10 healthy subjects in different positions. To compare performance of methods to respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for our strategy is 4.46 ± 3.68% with and agreement with respect of the reference of ≈ 98%.
This work presents a watermarking algorithm applied to medical images of COVID-19 patients, intending to preserve its diagnose and that it not be modified when watermark will be inserted. Besides, we tried to protect the information of the patient using an imperceptible watermarking. Our technique is based on a perceptive approach to insert the watermark by decomposing the medical image using the Hermite transform. We use as watermark two image logos, including text strings to demonstrate that the watermark can contain relevant information of the patient. Some metrics were applied to evaluate the performance of the algorithm. Finally, we present some results about robustness with some attacks applied to watermark images.
In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and a system based on a Convolutional Neural Network (CNN). After the magnification procedure, a CNN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. Two strategies are used as input to the CNN. A CNN-ROI proposal where a region of interest is selected manually on the image frame and in the second case, a CNN-Whole-Image proposal where the entire image frame is selected. Finally, the RR is estimated from the classified frames. The CNN-ROI proposal is tested on five subjects in lying face down position and it is compared to a procedure using different image processing steps to tag the frames as inhalation or exhalation. The mean average error in percentage obtained for this proposal is 2.326±1.144%. The CNN-whole-image proposal is tested on eight subjects in lying face down position. The mean average error in percentage obtained for this proposal is 2.115 ± 1.135%.
In this paper we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion magnification technique and a system based on different images processing steps. After the magnification procedure, a ROI is selected manually, an enhancement algorithm based on an adaptive histogram equalization is applied and finally the frames are binarized using the Otsu algorithm. Morphological operations are carry out on the video frames and a tracking temporal strategy is implemented to estimate the breathing rate. The magnification procedure was carried out using an Hermite decomposition. We have tested the method on three subjects in four positions (seat, lying face down, lying face up and lying in fetal position). The motion magnification approach is compared to the Laplacian decomposition strategy computing the mean absolute error.
In this paper we present an Eulerian motion magnification technique using a spatial decomposition based on the Steered Hermite Transform (SHT) which is inspired in the Human Vision System (HVS). We test our method in one sequence of the breathing of a newborn baby and on a video sequence that shows the heartbeat on the wrist. We estimate the heart pulse applying the Fourier transform on the magnified sequences. Our motion magnification approach is compared to the Laplacian and the Cartesian Hermite decomposition strategies by means of quantitative metrics.
We present an Eulerian motion magnification technique with a spatial decomposition based on the Hermite
Transform (HT). We compare our results to the approach presented in.1 We test our method in one sequence of
the breathing of a newborn baby and on an MRI left ventricle sequence. Methods are compared using quantitative
and qualitative metrics after the application of the motion magnification algorithm.
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