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 72
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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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A non-contact SpO2 estimation using a video magnification technique

2021 , Brieva, Jorge , Moya-Albor, Ernesto , Ponce, Hiram

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 channel of the image frame. We have tested the method on five healthy subjects using videos obtained from the googlemeet video conference platform. Each video includes the subject and the data of the contact pulse oximeter device. 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%. These preliminary results are promising for the implementation in a remote medical consultation setting. © 2021 SPIE.

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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 : Preface

2020 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , Brieva, Jorge

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. ©2020 Springer Nature Switzerland AG.

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Optical flow estimation in cardiac CT images using the steered Hermite transform

2013 , Ernesto Moya-Albor , Boris Escalante-Ramírez , Enrique Vallejo

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Data-Driven Innovation for Intelligent Technology : Perspectives and Applications in ICT

2024 , Ponce, Hiram , Brieva, Jorge , Lozada Flores, Octavio , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , Hiram Ponce , Jorge Brieva , Octavio Lozada-Flores , Lourdes Martínez-Villaseñor , Ernesto Moya-Albor

This book focuses on new perspectives and applications of data-driven innovation technologies, applied artificial intelligence, applied machine learning and deep learning, data science, and topics related to transforming data into value. It includes theory and use cases to help readers understand the basics of data-driven innovation and to highlight the applicability of the technologies. It emphasizes how the data lifecycle is applied in current technologies in different business domains and industries, such as advanced materials, healthcare and medicine, resource optimization, control and automation, among others. This book is useful for anyone interested in data-driven innovation for smart technologies, as well as those curious in implementing cutting-edge technologies to solve impactful artificial intelligence, data science, and related information technology and communication problems. ©Springer. ©The authors. ©The editors

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Adversarial Validation in Image Classification Datasets by Means of Cumulative Spectral Gradient

2024 , Diego Renza , Moya-Albor, Ernesto , Adrian Chavarro

The main objective of a machine learning (ML) system is to obtain a trained model from input data in such a way that it allows predictions to be made on new i.i.d. (Independently and Identically Distributed) data with the lowest possible error. However, how can we assess whether the training and test data have a similar distribution? To answer this question, this paper presents a proposal to determine the degree of distribution shift of two datasets. To this end, a metric for evaluating complexity in datasets is used, which can be applied in multi-class problems, comparing each pair of classes of the two sets. The proposed methodology has been applied to three well-known datasets: MNIST, CIFAR-10 and CIFAR-100, together with corrupted versions of these. Through this methodology, it is possible to evaluate which types of modification have a greater impact on the generalization of the models without the need to train multiple models multiple times, also allowing us to determine which classes are more affected by corruption.

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Contactless Video-Based Vital-Sign Measurement Methods: A Data-Driven Review

2024-01-01 , Brieva, Jorge , Moya-Albor, Ernesto , Ponce, Hiram , Escobedo-Gordillo, Andrés

Nowadays, the healthcare is a priority for both governments and persons. Vital sign monitoring allows knowing the health status and is widely used for prevention, diagnosis, and treatment of determined illnesses. In particular, breathing and heart rate are traditionally considered the most relevant and accessible vital signs. However, oxygen saturation was essential in the COVID-19 pandemic. On the other hand, contact techniques to estimate these vital signs are a standard monitoring reference. However, non-contact estimation methods have gained relevance in the last few years in those cases where there is the possibility of suffering stress, pain, and skin irritation in specific situations, as in the case of vulnerable skin in burn patients and neonates. In this chapter, a review of contactless video-based vital-sign methods is presented. The selected methods have a data-driven approach as an alternative when there is not theoretical model of the physiological phenomenon. Finally, a new framework with a general data-driven approach to estimate the most used vital signs is proposed. This framework includes a region of interest extraction stage, a video magnification technique to reveals subtle changes, and a machine learning method to estimate the vital signs. In addition, each step describes some recommendations and best practices found ©Springer.

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Image analysis applied to a freeze-concentration water purification system

2017 , Pardo Benito, José Mauricio , Moya-Albor, Ernesto , Ortega Ibarra, Germán , Brieva, Jorge

Freeze concentration is a promising water purification technology, specially due to the low levels of energy consumption when compared with other processes such as evaporation. Nevertheless, its development is still at pilot plant scale and more knowledge of the process is needed. One of the important parameters needed for design and control of the process is the growth velocity of the crystal, commonly known as the limit velocity. Above this velocity ice crystals will entrap solids and separation will not be successful. In this work, we describe a non-invasive technique to measure this limit velocity based on image analysis. Photographs were taken every 30 minutes in an experimental rig that allowed to freeze the sample unidirectionally. Back lighting was used in order to observe variations in luminosity at the samples surface. Image processing algorithms were used to segment the image, to identify the freezing front and to model its movement. Under the experimental conditions of this work, it was determined that the limiting freezing front velocity was 1.17 mm/h, which is a valuable information for the design and control of the process. © 2018 IMEKO-International Measurement Federation Secretariat. All rights reserved.

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Modeling of the major temporal arcade using genetic algorithms and orthogonal polynomials

2023 , Soto-Alvarez, Jose Alfredo , Cruz-Aceves, Ivan , Hernandez-Aguirre, Arturo , Hernandez-Gonzalez, Martha Alicia , Lopez-Montero, Luis Miguel , Moya-Albor, Ernesto , Brieva, Jorge , Gomez-Coronel, Sandra L. , Pérez-Daniel, Karina Ruby

Nowadays eye diseases that are not treated in a timely manner can lead to blindness in the patient. Diabetic retinopathy and retinopathy of prematurity are a couple of conditions considered to be the main causes of blindness in both adults and children. The technique used to date to verify the status of the retina is a qualitative analysis by an ophthalmological expert of fundus images. However, this is entirely based on the experience acquired by the physician and being able to detect changes in the vascular structure of the retina is a great challenge which can be addressed through technology. This paper presents a novel method to carry out the numerical modeling of the major temporal arcade using orthogonal polynomials of Legendre, Chebyshev and Laguerre through a genetic algorithm that helps to determine the coefficients of the linear combination of each one. A set of twenty fundus images already outlined by an expert was used, which were processed by the algorithm, generating an adjustment curve on the set of pixels of the Major Temporal Arcade. The results obtained were compared with three existing methodologies in the literature by using two metrics, emerging the Legendre polynomials as the most suitable for modeling, as a consequence of the low values obtained in the metrics compared to the other methods. © 2023 SPIE.

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