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 19
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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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Segmentation and optical flow estimation in cardiac CT sequences based on a spatiotemporal PDM with a correction scheme and the Hermite transform

2016 , Barba-J., Leiner , Moya-Albor, Ernesto , Escalante-Ramírez, Boris , Brieva, Jorge , Vallejo Venegas, Enrique

Purpose: The left ventricle and the myocardium are two of the most important parts of the heart used for cardiac evaluation. In this work a novel framework that combines two methods to isolate and display functional characteristics of the heart using sequences of cardiac computed tomography (CT) is proposed. A shape extraction method, which includes a new segmentation correction scheme, is performed jointly with a motion estimation approach.Methods: For the segmentation task we built a Spatiotemporal Point Distribution Model (STPDM) that encodes spatial and temporal variability of the heart structures. Intensity and gradient information guide the STPDM. We present a novel method to correct segmentation errors obtained with the STPDM. It consists of a deformable scheme that combines three types of image features: local histograms, gradients and binary patterns. A bio-inspired image representation model based on the Hermite transform is used for motion estimation. The segmentation allows isolating the structure of interest while the motion estimation can be used to characterize the movement of the complete heart muscle.Results: The work is evaluated with several sequences of cardiac CT. The left ventricle was used for evaluation. Several metrics were used to validate the proposed framework. The efficiency of our method is also demonstrated by comparing with other techniques.Conclusion: The implemented tool can enable physicians to better identify mechanical problems. The new correction scheme substantially improves the segmentation performance. Reported results demonstrate that this work is a promising technique for heart mechanical assessment. © 2016 Elsevier Ltd.

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An Edge Detection Method using a Fuzzy Ensemble Approach

2017 , Moya-Albor, Ernesto , Ponce, Hiram , Brieva, Jorge

Edge detection is one of the most important low level steps in image processing. In this work we propose a fuzzy ensemble based method for edge detection including a fuzzy c-means (FCM) approach to define the input membership functions of the fuzzy inference system (FIS). We tested the performance of the method using a public database with ground truth. Also, we compared our proposal with classical and other fuzzy based methods, using F-measure curves and the precision metric. We conducted experiments with different levels of salt & pepper noise to evaluate the performance of the edge detectors. The metrics illustrate the robustness of the choice of the threshold in the binarization step using this fuzzy ensemble method. In noisy conditions, the proposed method works better than other fuzzy approaches. Comparative results validated that our proposal overcomes traditional techniques. © 2017, Budapest Tech Polytechnical Institution. All rights reserved.

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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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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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A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset

2019 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Moya-Albor, Ernesto , Martinez-Villaseñor, Lourdes , Brieva, Jorge , Gutiérrez, Sebastián

The automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-based approach for fall detection and classification systems due to the recent exponential increase in the use of cameras. Moreover, deep learning techniques have revolutionized vision-based approaches. These techniques are considered robust and reliable solutions for detection and classification problems, mostly using convolutional neural networks (CNNs). Recently, our research group released a public multimodal dataset for fall detection called the UP-Fall Detection dataset, and studies on modality approaches for fall detection and classification are required. Focusing only on a vision-based approach, in this paper, we present a fall detection system based on a 2D CNN inference method and multiple cameras. This approach analyzes images in fixed time windows and extracts features using an optical flow method that obtains information on the relative motion between two consecutive images. We tested this approach on our public dataset, and the results showed that our proposed multi-vision-based approach detects human falls and achieves an accuracy of 95.64% compared to state-of-the-art methods with a simple CNN network architecture. © 2019 Elsevier Ltd

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Freezing front velocity estimation using image processing techniques

2020 , Pardo Benito, José Mauricio , Moya-Albor, Ernesto , Ortega Ibarra, Germán , Brieva, Jorge

Freeze concentration is a promising water purification technology due to its low energy consumption when compared with traditional procedures such as evaporation. Crystal growth velocity is an important parameter for the design and control of this process. If crystal growth surpasses certain speed, known as limit velocity, the separation process will not be successful. In this work two different motion detection image analysis strategies were used as non invasive techniques to follow the crystal growth velocity in a unidirectional crystallizer. The first technique is based on matching primitives detected on the image and the second one on optical flow algorithms. A mid-level processing algorithm has been used to identify the freezing front position. It segments the images using thresholding limits based on CIELAB color space parameters L*,a*,b*. Both methods were successfully used to estimate limit ice front velocity. Furthermore, the effect of initial solid concentration on limit ice front velocity has been modelled by an equation of the form Vl=K1C0 -k2. © 2019 Elsevier Ltd.

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Image Encryption and Decryption System through a Hybrid Approach Using the Jigsaw Transform and Langton’s Ant Applied to Retinal Fundus Images

2021 , Romero Arellano, Andrés Gabriel , Moya-Albor, Ernesto , Brieva, Jorge , Cruz-Aceves, Ivan , Avina-Cervantes, Juan Gabriel , Hernandez-Gonzalez, Martha Alicia , Lopez-Montero, Luis Miguel

In this work, a new medical image encryption/decryption algorithm was proposed. It is based on three main parts: the Jigsaw transform, Langton’s ant, and a novel way to add deterministic noise. The Jigsaw transform was used to hide visual information effectively, whereas Langton’s ant and the deterministic noise algorithm give a reliable and secure approach. As a case study, the proposal was applied to high-resolution retinal fundus images, where a zero mean square error was obtained between the original and decrypted image. The method performance has been proven through several testing methods, such as statistical analysis (histograms and correlation distributions), entropy computation, keyspace assessment, robustness to differential attack, and key sensitivity analysis, showing in each one a high security level. In addition, the method was compared against other works showing a competitive performance and highlighting with a large keyspace (>1×101,134,190.38). Besides, the method has demonstrated adequate handling of high-resolution images, obtaining entropy values between 7.999988 and 7.999989, an average Number of Pixel Change Rate (NPCR) of 99.5796%±0.000674, and a mean Uniform Average Change Intensity (UACI) of 33.4469%±0.00229. In addition, when there is a small change in the key, the method does not give additional information to decrypt the image. ©2021 Axioms, MDPI.

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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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A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT

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

Currently, algorithms to embed watermarks into digital images are increasing exponentially, for example in image copyright protection. However, when a watermarking algorithm is applied, the preservation of the image’s quality is of utmost importance, for example in medical images, where improper embedding of the watermark could change the patient’s diagnosis. On the other hand, in digital images distributed over the Internet, the owner of the images must also be protected. In this work, an imperceptible, robust, secure, and hybrid watermarking algorithm is presented for copyright protection. It is based on the Hermite Transform (HT) and the Discrete Cosine Transform (DCT) as a spatial–frequency representation of a grayscale image. Besides, it uses a block-based strategy and a perfectibility analysis of the best embedding regions inspired by the Human Vision System (HVS), giving the imperceptibility of the watermark, and a Singular-Value Decomposition (SVD) approach improved robustness against attacks. In addition, the proposed method can embed two watermarks, a digital binary image (LOGO) and information about the owner and the technical data of the original image in text format (MetaData). To secure both watermarks, the proposed method uses the Jigsaw Transform (JST) and the Elementary Cellular Automaton (ECA) to encrypt the image LOGO and a random sequence generator and the XOR operation to encrypt the image MetaData. On the other hand, the proposed method was tested using a public dataset of 49 grayscale images to assess the effectiveness of the watermark embedding and extraction procedures. Furthermore, the proposed watermarking algorithm was evaluated under several processing and geometric algorithms to demonstrate its robustness to the majority, intentional or unintentional, attacks, and a comparison was made with several state-of-the-art techniques. The proposed method obtained average values of PSNR = 40.2051 dB, NCC = 0.9987, SSIM = 0.9999, and MSSIM = 0.9994 for the watermarked image. In the case of the extraction of the LOGO, the proposal gave MSE = 0, PSNR ≫ 60 dB, NCC = 1, SSIM = 1, and MSSIM = 1, whereas, for the image MetaData extracted, it gave BER = 0% and (Formula presented.). Finally, the proposed encryption method presented a large key space ((Formula presented.)) for the LOGO image. © 2023 by the authors. Multidisciplinary Digital Publishing Institute (MDPI).

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