Brieva, Jorge
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Brieva, Jorge
Official Name
Brieva Rico, Jorge Eduardo
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jbrieva
Brieva, J.
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Scopus Author ID
7005997081
Researcher ID
GMW-951-2022

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Now showing 1 - 10 of 69
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3D optical flow estimation in cardiac CT images using the Hermite transform

2017 , Moya-Albor, Ernesto , Mira, Carlos , Brieva, Jorge , Escalante-Ramírez, Boris , Vallejo Venegas, Enrique

Heart diseases are one of the most important causes of death in the Western world. It is, then, important to implement algorithms to aid the specialist in analyzing the heart motion. We propose a new strategy to estimate the cardiac motion through a 3D optical flow differential technique that uses the Steered Hermite transform (SHT). SHT is a tool that performs a decomposition of the images in a base that model the visual patterns used by the human vision system (HSV) for processing the information. The 3D + t analysis allows to describe most of motions of the heart, for example, the twisting motion that takes place on every beat cycle and to identify abnormalities of the heart walls. Our proposal was tested on two phantoms and on two sequences of cardiac CT images corresponding to two different patients. We evaluate our method using a reconstruction schema, for this, the resulting 3D optical flow was applied over the volume at time t to obtain a estimated volume at time t + 1. We compared our 3D optical flow approach to the classical Horn and Shunk's 3D algorithm for different levels of noise. © 2017 SPIE.

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3D Hermite Transform Optical Flow Estimation in Left Ventricle CT Sequences

2020 , Mira, Carlos , Moya-Albor, Ernesto , Escalante-Ramírez, Boris , Olveres, Jimena , Brieva, Jorge , Vallejo, Enrique

Heart diseases are the most important causes of death in the world and over the years, the study of cardiac movement has been carried out mainly in two dimensions, however, it is important to consider that the deformations due to the movement of the heart occur in a three-dimensional space. The 3D + t analysis allows to describe most of the motions of the heart, for example, the twisting motion that takes place on every beat cycle that allows us identifying abnormalities of the heart walls. Therefore, it is necessary to develop algorithms that help specialists understand the cardiac movement. In this work, we developed a new approach to determine the cardiac movement in three dimensions using a differential optical flow approach in which we use the steered Hermite transform (SHT) which allows us to decompose cardiac volumes taking advantage of it as a model of the human vision system (HVS). Our proposal was tested in complete cardiac computed tomography (CT) volumes ( 3D + t), as well as its respective left ventricular segmentation. The robustness to noise was tested with good results. The evaluation of the results was carried out through errors in forwarding reconstruction, from the volume at time t to time t + 1 using the optical flow obtained (interpolation errors). The parameters were tuned extensively. In the case of the 2D algorithm, the interpolation errors and normalized interpolation errors are very close and below the values reported in ground truth flows. In the case of the 3D algorithm, the results were compared with another similar method in 3D and the interpolation errors remained below 0.1. These results of interpolation errors for complete cardiac volumes and the left ventricle are shown graphically for clarity. Finally, a series of graphs are observed where the characteristic of contraction and dilation of the left ventricle is evident through the representation of the 3D optical flow. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

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Distributed evolutionary learning control for mobile robot navigation based on virtual and physical agents

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

This paper presents a distributed evolutionary learning control based on social wound treatment for mobile robot navigation using an integrated multi-robot system comprised of simulated and physical robots. To do so, this work proposes an extension of the population-based metaheuristic wound treatment optimization (WTO) method into a distributed scheme. In addition, this distributed WTO method is implemented on the multi-robot system allowing them to experience the environment in their own and communicate their findings, resulting in an emergence intelligence. We implemented our proposal using the combination of five simulated robots with one physical robot for tuning a navigation controller to move freely in a workspace. Results showed that the solution found by this multi-robot system aims using the output controller in the physical robot for successfully achieving the goal to move the robot around a U-maze, without applying any transfer learning approach. We consider this proposal useful in evolutionary robotics, and of great importance to decrease the gap related to transfer knowledge in robotics from simulation to reality. © 2019 Elsevier B.V.

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A string matching shape prior to constraint the level set evolution for the segmentation of x-ray coronary angiography

2013 , Brieva, Jorge

This paper presents a shape based level set technique to extract vascular structures in X-Ray Angiographic images. It makes use of the Mumford-Shah functional19 applied to images of non-uniform illumination. The shape model is computed using string matching techniques from a preliminary hierarchical multiphase method. Its performance, using different metrics, has been evaluated on a set of three angiographic image sequences by comparison with manual delineation. A sensitivity of 91.31 ± 3.52% and a specificity of 94.28 ± 9.17% were found in the quantitative validation analysis. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

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Mobile Robot with Movement Detection Controlled by a Real-Time Optical Flow Hermite Transform

2015 , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram

This chapter presents a new algorithm inspired in the human visual system to compute optical flow in real-time based on the Hermite Transform. This algorithm is applied in a vision-based control system for a mobile robot. Its performance is compared for different texture scenarios with the classical Horn and Schunck algorithm. The design of the nature-inspired controller is based on the agent-environment model and agent’s architecture. Moreover, a case study of a robotic system with the proposed real-time Hermite optical flow method was implemented for braking and steering when mobile obstacles are close to the robot. Experimental results showed the controller to be fast enough for real-time applications, be robust to different background textures and colors, and its performance does not depend on inner parameters of the robotic system. © Springer International Publishing Switzerland 2016.

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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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Color Image Encryption Algorithm Based on a Chaotic Model Using the Modular Discrete Derivative and Langton’s Ant

2023 , Moya-Albor, Ernesto , Romero Arellano, Andrés Gabriel , Brieva, Jorge , Gomez-Coronel, Sandra L.

In this work, a color image encryption and decryption algorithm for digital images is presented. It is based on the modular discrete derivative (MDD), a novel technique to encrypt images and efficiently hide visual information. In addition, Langton’s ant, which is a two-dimensional universal Turing machine with a high key space, is used. Moreover, a deterministic noise technique that adds security to the MDD is utilized. The proposed hybrid scheme exploits the advantages of MDD and Langton’s ant, generating a very secure and reliable encryption algorithm. In this proposal, if the key is known, the original image is recovered without loss. The method has demonstrated high performance through various tests, including statistical analysis (histograms and correlation distributions), entropy, texture analysis, encryption quality, key space assessment, key sensitivity analysis, and robustness to differential attack. The proposed method highlights obtaining chi-square values between (Formula presented.) and (Formula presented.), entropy values between (Formula presented.) and (Formula presented.), PSNR values (in the original and encrypted images) between (Formula presented.) and (Formula presented.), the number of pixel change rate (NPCR) values between (Formula presented.) and (Formula presented.), unified average changing intensity (UACI) values between (Formula presented.) and (Formula presented.), and a vast range of possible keys (Formula presented.). On the other hand, an analysis of the sensitivity of the key shows that slight changes to the key do not generate any additional information to decrypt the image. In addition, the proposed method shows a competitive performance against recent works found in the literature. © 2023 by the authors.

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High-Dimensional Feature Selection for Automatic Classification of Coronary Stenosis Using an Evolutionary Algorithm

2024 , Gil-Rios, Miguel-Angel , Cruz-Aceves, Ivan , Hernandez-Aguirre, Arturo , Moya-Albor, Ernesto , Brieva, Jorge , Hernandez-Gonzalez, Martha-Alicia , Solorio-Meza, Sergio-Eduardo

In this paper, a novel strategy to perform high-dimensional feature selection using an evolutionary algorithm for the automatic classification of coronary stenosis is introduced. The method involves a feature extraction stage to form a bank of 473 features considering different types such as intensity, texture and shape. The feature selection task is carried out on a high-dimensional feature bank, where the search space is denoted by O(2𝑛) and 𝑛=473. The proposed evolutionary search strategy was compared in terms of the Jaccard coefficient and accuracy classification with different state-of-the-art methods. The highest feature selection rate, along with the best classification performance, was obtained with a subset of four features, representing a 99%99% discrimination rate. In the last stage, the feature subset was used as input to train a support vector machine using an independent testing set. The classification of coronary stenosis cases involves a binary classification type by considering positive and negative classes. The highest classification performance was obtained with the four-feature subset in terms of accuracy (0.86)(0.86) and Jaccard coefficient (0.75)(0.75) metrics. In addition, a second dataset containing 2788 instances was formed from a public image database, obtaining an accuracy of 0.890.89 and a Jaccard Coefficient of 0.800.80. Finally, based on the performance achieved with the four-feature subset, they can be suitable for use in a clinical decision support system. ©© 2024 by the authors. Licensee MDPI, Basel, Switzerland. MDPI

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Portable Device for Monitoring the Respiratory Rate in Home Conditions

2023 , Arizmendi Flores, Sergio Alejandro , Contreras Fernandez Del Castillo, Carlos , Solana Carreón, Diego Santiago , Zenteno Barreiro, Sebastián , Brieva, Jorge , Ponce, Hiram , Moya-Albor, Ernesto

During the last decades, technological advances have escalated at astronomical levels. One of the fields where technology has proven to be the most beneficial is in medicine. The vital signs monitoring is one of the medical areas with greater advances. Despite this technological advance in monitoring vital signs, heart rate and oxygen saturation are the ones that continue to be most used in diagnosis to the detriment of respiratory rate (RR) in medical and non medical environments. In this work we propose a preliminary contact device easy to use in non medical environments for monitoring, measuring and recording the RR. We test our device in controlled conditions in five healthy subjects. Accuracy will be computed through comparison of a visible estimation of the RR and the acquired data. The obtained mean PE% is less than 6% in normal conditions of utilisation for the experiments. Overall, our device has proven a reliable tool for RR measurement in this preliminary version. © 2023 IEEE.

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Bio-Inspired Watermarking Method for Authentication of Fundus Images in Computer-Aided Diagnosis of Retinopathy

2024 , Moya-Albor, Ernesto , Gomez-Coronel, Sandra L. , Brieva, Jorge , Lopez-Figueroa, Alberto

Nowadays, medical imaging has become an indispensable tool for the diagnosis of some pathologies and as a health prevention instrument. In addition, medical images are transmitted over all types of computer networks, many of them insecure or susceptible to intervention, making sensitive patient information vulnerable. Thus, image watermarking is a popular approach to embed copyright protection, Electronic Patient Information (EPR), institution information, or other digital image into medical images. However, in the medical field, the watermark must preserve the quality of the image for diagnosis purposes. In addition, the inserted watermark must be robust both to intentional and unintentional attacks, which try to delete or weaken it. This work presents a bio-inspired watermarking algorithm applied to retinal fundus images used in computer-aided retinopathy diagnosis. The proposed system uses the Steered Hermite Transform (SHT), an image model inspired by the Human Vision System (HVS), as a spread spectrum watermarking technique, by leveraging its bio-inspired nature to give imperceptibility to the watermark. In addition, the Singular Value Decomposition (SVD) is used to incorporate the robustness of the watermark against attacks. Moreover, the watermark is embedded into the RGB fundus images through the blood vessel patterns extracted by the SHT and using the luma band of Y’CbCr color model. Also, the watermark was encrypted using the Jigsaw Transform (JST) to incorporate an extra level of security. The proposed approach was tested using the image public dataset MESSIDOR-2, which contains 1748 8-bit color images of different sizes and presenting different Diabetic Retinopathy (DR). Thus, on the one hand, in the experiments we evaluate the proposed bio-inspired watermarking method over the entire MESSIDOR-2 dataset, showing that the embedding process does not affect the quality of the fundus images and the extracted watermark, by obtaining average Peak Signal-to-Noise Ratio (PSNR) values higher to 53 dB for the watermarked images and average PSNR values higher to 32 dB to the extracted watermark for the entire dataset. Also, we tested the method against image processing and geometric attacks successfully extracting the watermarking. A comparison of the proposed method against state-of-the-art was performed, obtaining competitive results. On the other hand, we classified the DR grade of the fundus image dataset using four trained deep learning models (VGG16, ResNet50, InceptionV3, and YOLOv8) to evaluate the inference results using the originals and marked images. Thus, the results show that DR grading remains both in the non-marked and marked images. ©MDPI

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