Moya-Albor, Ernesto
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Moya-Albor, Ernesto
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Moya Albor, Ernesto
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emoya
Moya-Albor, E.
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55523223700
Researcher ID
J-1891-2019

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Now showing 1 - 10 of 75
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Non-contact breathing rate monitoring system based on a Hermite video magnification technique

2018 , Brieva, Jorge , Moya-Albor, Ernesto , Yael Rivas Scott, Orlando , Ponce, Hiram

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 di€erent 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. © SPIE. Downloading of the abstract is permitted for personal use only.

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Nuclear density analysis in microscopic images for the characterization of retinal geographic atrophy

2020 , Peralta Ildefonso, Martha Janneth , Moya-Albor, Ernesto , Brieva, Jorge , Lira, Esmeralda , Pérez Ortiz, Andric Christopher , Coral-Vázquez, Ramón , Estrada Mena, Francisco Javier

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in industrialized countries. It is estimated that AMD affects at least 1 in 10 Hispanics. Previous reports have shown that AMD has multiple risk factors. Recently, we demonstrated that some genetic variants in the SGCD gene are involved in AMD developments, especially in early-stage (geographic atrophy, GA). Therefore, to evaluate the relationship between SGCD's absence and the loss of photoreceptors in GA, we worked with a genetically modified mouse model, SGCD deficient (Sgcd-/-) and a control mouse C57BL/6J (Sgcd+/+). First, we obtained hematoxylin and eosin (H&E) retina staining microscopic images. Then, we coarsely selected the outer and inner nuclear retinal layer (ONL and INL respectively) and finally, we applied an automatic nuclei segmentation to calculate the nuclear density in each region. Our results showed that Sgcd absence does not result in photoreceptor loss, on the contrary, it promotes an increment in nuclear density by 8.7% in ONL and 20.1% in INL compared with control eyes (p = 0.0033 and p < 0.0001 respectively). This could be explained by the fact that SGCD codifies the delta-sarcoglycan protein and there is evidence that showed a relationship between the absence of this protein with the activation of a cell proliferation signaling pathway. Finally, our results show that the delta-sarcoglycan protein could play an important role in the pathogenesis of the geographic atrophy. Moreover, there are promising perspectives for the systematic approach applied for cell image analysis, as an important tool to determine the nuclear density for assessing the progression of AMD. ©COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

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Challenges and trends in multimodal fall detection for healthcare

2020 , Ponce, Hiram , Brieva, Jorge , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , HIRAM EREDIN PONCE ESPINOSA;376768 , JORGE EDUARDO BRIEVA RICO;121435

This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion. It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples. This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human–machine interaction, among others.

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Non-contact breathing rate monitoring system using a magnification technique and convolutional networks

2020 , Brieva, Jorge , Ponce, Hiram , Moya-Albor, Ernesto

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%. © COPYRIGHT 2020 SPIE. Downloading of the abstract is permitted for personal use ONLY.

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Secure medical image encryption approach based on Langton's ant and jigsaw transform

2021 , Moya-Albor, Ernesto , Romero Arellano, Andrés Gabriel , Brieva, Jorge , Cruz-Aceves, Ivan , Avina-Cervantes, Juan G. , Hernández González, Martha A. , López Montero, Luis M.

In this work, we propose a medical image encryption method. It is based on the Jigsaw transform, cyclic permutations, and a deterministic noise approach to hide visual information in the images. On the other hand, Langton's ant is used to encrypt the images. The method proposed was tested on fundus retinal photographs. The robustness of our algorithm has been proven through several testing, such as statistical analysis (histograms and correlation distributions), visual testing, entropy testing, and keyspace assessment, showing in each one a high-security level. © 2021 SPIE.

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Motion estimation and segmentation in CT cardiac images using the Hermite transform and active shape models

2013 , Boris Escalante-Ramírez , Ernesto Moya-Albor , Leiner Barba-J , Fernando Arambula Cosio , Enrique Vallejo , Andrew G. Tescher

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Evaluation of Dataset Distribution in Biomedical Image Classification Against Image Acquisition Distortions

2024 , Aguilera-González, Santiago , Renza, Diego , Moya-Albor, Ernesto

One of the conditions expected when training a machine learning model is that the inference data should be independently and identically distributed (i.i.d.) with respect to the training data. However, as the real world evolves, this condition can be lost, which is known as shift distribution. This situation can affect the performance of a machine learning model, so the question is how to evaluate (without training a model) the presence of shift distribution. Consequently, this paper presents a proposal to determine the degree of distribution shift in medical image datasets in the face of possible distortions due to the capture system. The methodology is based on Cumulative Spectral Gradient (CSG) metric and it is applied to three biomedical imaging datasets extracted from MedMNIST, an initiative that has compiled several standardized biomedical datasets: PneumoniaMNIST, BreastMNIST and RetinaMNIST. Thanks to this methodology, it is possible to evaluate which types of modifications have a greater impact on the generalization of the models, as well as to determine if there are classes more affected by corruptions. ©The authors ©IEEE.

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Video motion magnification for monitoring of vital signals using a perceptual model

2017 , Brieva, Jorge , Moya-Albor, Ernesto , Gomez-Coronel, Sandra L. , Ponce, Hiram

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. © 2017 SPIE.

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Hybrid Methods to Quantify Ice Front Movement during Freeze-concentration Process

2018 , Moya-Albor, Ernesto , Pardo Benito, José Mauricio , Gregori, Andres , Brieva, Jorge

Freeze concentration is an emerging separation method that can be implemented as part of a water purification system, however, its development is still at pilot plant scale. One of the useful parameters for design and control of this process is the growth velocity of the crystal, commonly known as the limit velocity. Above this limit, ice crystals will capture solids and separation will lose quality. Two-hybrid no invasive methods to follow the displacement of the ice front during a freeze concentration (FC) procedure has been tested.The methods included image segmentation, string matching technique and a bio-inspired optical flow algorithm to calculate the ice front velocity. A thermal camera was used in the experiments in order to validate the estimated movement of the ice by the image analysis procedures. Both methods were successful to follow the movement of the ice front, and the estimated displacement was between 4 and 12 pixels in 15 minutes. © 2018 IEEE.

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Stair Climbing Robot Based on Convolutional Neural Networks for Visual Impaired

2019 , Campos, Guillermo , Poza, David , Reyes, Moises , Zacate, Alma , Ponce, Hiram , Brieva, Jorge , Moya-Albor, Ernesto

When a person loses the sense of sight, in general, it is suggested to use a white cane to perform daily activities. However, using a white cane limits the movement of a person. In addition, guide dogs can be served in this impairment. However, the acquisition and maintenance of a guide dog is extremely high for people in development countries. In this regard, this paper presents a proof-of-concept of a low-cost robotic system able to guide a visual impaired, as a guide dog. The robot is specially designed for climbing stairs at indoors, and it uses convolutional neural networks (CNN) for both object detection and hand gesture recognition for special instructions from the user. Experimental results showed that our prototype robot can climb stairs with 86.7% of efficiency in concrete stair surfaces. Also, the visual representation by CNN performed more than 98% accuracy. © 2019 IEEE.

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