Persona: Carmona Suárez, Enrique J.
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Carmona Suárez
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Enrique J.
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Publicación Semantic-guided generative latent diffusion augmentation approaches for improving the neovascularization diagnosis in OCT-A imaging(ELSEVIER, 2025-01-13) Iglesias Morís, Daniel; Moura, Joaquim de; Novo, Jorge; Ortega, Marcos; Carmona Suárez, Enrique J.Age-related Macular Degeneration (AMD) presents an enormous challenge in Western Societies due to the increase in life expectancy. AMD is characterized for causing Macular Neovascularization. Optical Coherence Tomography Angiography (OCT-A) represents an advanced method to help find evidence of the disease. In this context, deep learning algorithms are suitable to make a screening of the disease. However, biomedical imaging domains are usually affected by the data scarcity issue. The mitigation of this problem can be achieved with the support of generative latent diffusion models. This represents a powerful strategy to artificially augment the cardinality of the original dataset. In this work, we present a novel fully automatic methodology to generate OCT-A images, guided by semantic information, to reduce the impact of data scarcity and to enable an accurate neovascularization diagnosis. The evaluation has been performed with a specific dataset composed of two different fields of view commonly used by clinicians. The results demonstrate a top accuracy of 96.50% 1.37%, using 3 × 3 scans, and 95.79% 1.44%, when using 6 × 6 scans. The proposed methodology has great potential to be extrapolated to other imaging modalities and domains.Publicación A block-based model for monitoring of human activity(Elsevier, 2011-03) Folgado Zuñiga, Encarnación; Rincón Zamorano, Mariano; Bachiller Mayoral, Margarita; Carmona Suárez, Enrique J.The study of human activity is applicable to a large number of science and technology fields, such as surveillance, biomechanics or sports applications. This article presents BB6-HM, a block-based human model for real-time monitoring of a large number of visual events and states related to human activity analysis, which can be used as components of a library to describe more complex activities in such important areas as surveillance, for example, luggage at airports, clients’ behaviour in banks and patients in hospitals. BB6-HM is inspired by the proportionality rules commonly used in Visual Arts, i.e., for dividing the human silhouette into six rectangles of the same height. The major advantage of this proposal is that analysis of the human can be easily broken down into regions, so that we can obtain information of activities. The computational load is very low, so it is possible to define a very fast implementation. Finally, this model has been applied to build classifiers for the detection of primitive events and visual attributes using heuristic rules and machine learning techniques.Publicación Using genetic algorithms to improve the thermodynamic efficiency of gas turbines designed by traditional methods(Elsevier, 2012-11) Chaquet Ulldemolins, José María; Corral, Roque; Carmona Suárez, Enrique J.A method for optimizing the thermodynamic efficiency of aeronautical gas turbines designed by classical methods is presented. This method is based in the transformation of the original constrained optimization problem into a new constrained free optimization problem which is solved by a genetic algorithm. Basically, a set of geometric, aerodynamic and acoustic noise constraints must be fulfilled during the optimization process. As a case study, the thermodynamic efficiency of an already optimized by traditional methods real aeronautical low pressure turbine design of 13 rows has been successfully improved, increasing the turbine efficiency by 0.047% and reducing the total number of airfoils by 1.61%. In addition, experimental evidence of a strong correlation between the total number of airfoils and the turbine efficiency has been observed. This result would allow us to use the total number of airfoils as a cheap substitute of the turbine efficiency for a coarse optimization at the first design steps.Publicación On the effect of feedback in multilevel representation spaces for visual surveillance tasks(Elsevier, 2009-01) Martínez Campos, Javier; Mira Mira, José; Rincón Zamorano, Mariano; Bachiller Mayoral, Margarita; Martínez Tomás, Rafael; Carmona Suárez, Enrique J.In this work we propose a general top–down feedback scheme between adjacent description levels to interpret video sequences. This scheme distinguishes two types of feedback: repair-oriented feedback and focus-oriented feedback. With the first it is possible to improve the system's performance and produce more reliable and consistent information, and with the second it is possible to adjust the computational load to match the aims. Finally, the general feedback scheme is used in different examples for a visual surveillance application which improved the final result of each description level by using the information in the higher adjacent level.Publicación Fast detection of the main anatomical structures in digital retinal images based on intra-and inter-structure relational knowledge(Elsevier, 2017-10) Molina Casado, José María; García Feijoó, Julián; Carmona Suárez, Enrique J.Background and objective: The anatomical structure detection in retinal images is an open problem. However, most of the works in the related literature are oriented to the detection of each structure individually or assume the previous detection of a structure which is used as a reference. The objective of this paper is to obtain simultaneous detection of the main retinal structures (optic disc, macula, network of vessels and vascular bundle) in a fast and robust way. Methods: We propose a new methodology oriented to accomplish the mentioned objective. It consists of two stages. In an initial stage, a set of operators is applied to the retinal image. Each operator uses intra-structure relational knowledge in order to produce a set of candidate blobs that belongs to the desired structure. In a second stage, a set of tuples is created, each of which contains a different combination of the candidate blobs. Next, filtering operators, using inter-structure relational knowledge, are used in order to find the winner tuple. A method using template matching and mathematical morphology is implemented following the proposed methodology. Results: A success is achieved if the distance between the automatically detected blob center and the actual structure center is less than or equal to one optic disc radius. The success rates obtained in the different public databases analyzed were: MESSIDOR (99.33%, 98.58%, 97.92%), DIARETDB1 (96.63%, 100%, 97.75%), DRIONS (100%, n/a, 100%) and ONHSD (100%, 98.85%, 97.70%) for optic disc (OD), macula (M) and vascular bundle (VB), respectively. Finally, the overall success rate obtained in this study for each structure was: 99.26% (OD), 98.69% (M) and 98.95% (VB). The average time of processing per image was 4.16 ± 0.72 s. Conclusions: The main advantage of the use of inter-structure relational knowledge was the reduction of the number of false positives in the detection process. The implemented method is able to simultaneously detect four structures. It is fast, robust and its detection results are competitive in relation to other methods of the recent literature.Publicación Solving differential equations with Fourier series and Evolution Strategies(Elsevier, 2012-09) Chaquet Ulldemolins, José María ; Carmona Suárez, Enrique J.A novel mesh-free approach for solving differential equations based on Evolution Strategies (ESs) is presented. Any structure is assumed in the equations making the process general and suitable for linear and nonlinear ordinary and partial differential equations (ODEs and PDEs), as well as systems of ordinary differential equations (SODEs). Candidate solutions are expressed as partial sums of Fourier series. Taking advantage of the decreasing absolute value of the harmonic coefficients with the harmonic order, several ES steps are performed. Harmonic coefficients are taken into account one by one starting with the lower order ones. Experimental results are reported on several problems extracted from the literature to illustrate the potential of the proposed approach. Two cases (an initial value problem and a boundary condition problem) have been solved using numerical methods and a quantitative comparative is performed. In terms of accuracy and storing requirements the proposed approach outperforms the numerical algorithm.Publicación Modeling, localization, and segmentation of the foveal avascular zone on retinal OCT-angiography images(IEEE, 2020-08-17) Díaz González, Macarena; Novo, Jorge; Ortega, Marcos; Carmona Suárez, Enrique J.The Foveal Avascular Zone (FAZ) is a capillary-free area that is placed inside the macula and its morphology and size represent important biomarkers to detect different ocular pathologies such as diabetic retinopathy, impaired vision or retinal vein occlusion. Therefore, an adequate and precise segmentation of the FAZ presents a high clinical interest. About to this, Angiography by Optical Coherence Tomography (OCT-A) is a non-invasive imaging technique that allows the expert to visualize the vascular and avascular foveal zone. In this work, we present a robust methodology composed of three stages to model, localize, and segment the FAZ in OCT-A images. The first stage is addressed to generate two FAZ normality models: superficial and deep plexus. The second one uses the FAZ model as a template to localize the FAZ center. Finally, in the third stage, an adaptive binarization is proposed to segment the entire FAZ region. A method based on this methodology was implemented and validated in two OCT-A image subsets, presenting the second subset more challenging pathological conditions than the first. We obtained localization success rates of 100% and 96% in the first and second subsets, respectively, considering a success if the obtained FAZ center is inside the FAZ area segmented by an expert clinician. Complementary, the Dice score and other indexes (Jaccard index and Hausdorff distance) are used to measure the segmentation quality, obtaining competitive average values in the first subset: 0.84 ± 0.01 (expert 1) and 0.85 ± 0.01 (expert 2). The average Dice score obtained in the second subset was also acceptable (0.70 ± 0.17), even though the segmentation process is more complex in this case.Publicación Simultaneous segmentation of the optic disc and fovea in retinal images using evolutionary algorithms(Springer, 2021-03) Molina Casado, José María; Carmona Suárez, Enrique J.In this work, we present a new methodology to simultaneously segment anatomical structures in medical images. Additionally, this methodology is instantiated in a method that is used to simultaneously segment the optic disc (OD) and fovea in retinal images. The OD and fovea are important anatomical structures that must be previously identified in any image-based computer-aided diagnosis system dedicated to diagnosing retinal pathologies that cause vision problems. Basically, the simultaneous segmentation method uses an OD-fovea model and an evolutionary algorithm. On the one hand, the model is built using the intra-structure relational knowledge, associated with each structure, and the inter-structure relational knowledge existing between both and other retinal structures. On the other hand, the evolutionary algorithm (differential evolution) allows us to automatically adjust the instance parameters that best approximate the OD-fovea model in a given retinal image. The method is evaluated in the MESSIDOR public database. Compared with other recent segmentation methods in the related literature, competitive segmentation results are achieved. In particular, a sensitivity and specificity of 0.9072 and 0.9995 are respectively obtained for the OD. Considering a success when the distance between the detected and actual center is less than or equal to η times the OD radius, the success rates obtained for the fovea are 97.3% and 99.0% for η = 1=2 and η = 1 and, respectively. The segmentation average time per image is 29.35 s.Publicación Introducing modularity and homology in grammatical evolution to address the analog electronic circuit design problem(IEEE, 2020-08-24) Castejón, Federico; Carmona Suárez, Enrique J.We present a new approach based on grammatical evolution (GE) aimed at addressing the analog electronic circuit design problem. In the new approach, called multi-grammatical evolution (MGE), a chromosome is a variable-length codon string that is divided into as many partitions as subproblems result from breaking down the original optimization problem: circuit topology and component sizing in our case. This leads to a modular approach where the solution of each subproblem is encoded and evolved in a partition of the chromosome. Additionally, each partition is decoded according to a specific grammar and the final solution to the original problem emerges as an aggregation result associated with the decoding process of the different partitions. Modularity facilitates the encoding and evolution of the solution in each subproblem. On the other way, homology helps to reduce the potentially destructive effect associated with standard crossover operators normally used in GE-based approaches. Seven analog circuit designs are addressed by an MGE-based method and the obtained results are compared to those obtained by different methods based on GE and other evolutionary paradigms. A simple parsimony mechanism was also introduced to ensure compliance with design specifications and reduce the number of components of the circuits obtained. We can conclude that our method obtains competitive results in the seven circuits analyzed.Publicación Identification of the optic nerve head with genetic algorithms(Elsevier, 2008-07) García Feijoó, Julián; Martínez de la Casa, José M.; Rincón Zamorano, Mariano; Carmona Suárez, Enrique J.Objective This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms. Methods and material Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an eye fundus colour image as input, a set of hypothesis points was obtained that exhibited geometric properties and intensity levels similar to the ONH contour pixels. Next, a genetic algorithm was used to find an ellipse containing the maximum number of hypothesis points in an offset of its perimeter, considering some constraints. The ellipse thus obtained is the approximation to the ONH. The segmentation method is tested in a sample of 110 eye fundus images, belonging to 55 patients with glaucoma (23.1%) and eye hypertension (76.9%) and random selected from an eye fundus image base belonging to the Ophthalmology Service at Miguel Servet Hospital, Saragossa (Spain). Results and conclusions The results obtained are competitive with those in the literature. The method's generalization capability is reinforced when it is applied to a different image base from the one used in our study and a discrepancy curve is obtained very similar to the one obtained in our image base. In addition, the robustness of the method proposed can be seen in the high percentage of images obtained with a discrepancy δ < 5 (96% and 99% in our and a different image base, respectively). The results also confirm the hypothesis that the ONH contour can be properly approached with a non-deformable ellipse. Another important aspect of the method is that it directly provides the parameters characterising the shape of the papilla: lengths of its major and minor axes, its centre of location and its orientation with regard to the horizontal position.