Pablo Mesejo

Université d'Auvergne, Image Science for Interventional Techniques, Post-Doc

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Papers by Pablo Mesejo

A differential evolution-based approach for fitting a nonlinear biophysical model to fMRI BOLD data

IEEE Journal of Selected Topics in Signal Processing, 2015

Estimating Biophysical Parameters from BOLD Signals through Evolutionary-Based Optimization

Lecture Notes in Computer Science, 2015

Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy

IEEE Transactions on Medical Imaging, 2016

A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

Applied Soft Computing, 2016

An Analysis of Errors in Graph-Based Keypoint Matching and Proposed Solutions

Lecture Notes in Computer Science, 2014

First results and future developments of the MIBISOC Project in the IBISlab of the university of parma

Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12, 2012

Abstract Medical Imaging using Bio-Inspired and Soft Computing (MIBISOC) is a Marie Curie Initial... more Abstract Medical Imaging using Bio-Inspired and Soft Computing (MIBISOC) is a Marie Curie Initial Training Network (ITN) within the EU Seventh Framework Programme. MIBISOC is a training programme in which sixteen Early-Stage Researchers (ESRs) are exposed to a wide variety of Soft Computing (SC) and Bio-Inspired Computing (BC) techniques, and face the challenge of applying them to the different situations and problems which characterize medical image processing tasks. Hence, the main goal of the project is to generate new ...

A Comparative Study of Three GPU-Based Metaheuristics

Lecture Notes in Computer Science, 2012

In this paper we compare GPU-based implementations of three metaheuristics: Particle Swarm Optimi... more In this paper we compare GPU-based implementations of three metaheuristics: Particle Swarm Optimization, Differential Evolution, and Scatter Search. A GPU-based implementation, obviously, does not change the general properties of the algorithms. As well, we give for granted that GPU-based implementation of both algorithm and fitness function produces a significant speed-up with respect to a sequential implementation. Accordingly, the main goal of this work has been to fairly assess the efficiency of the GPU- ...

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GPU-Based Automatic Configuration of Differential Evolution: A Case Study

Lecture Notes in Computer Science, 2013

Algorithm configuration using GPU-based metaheuristics

Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion - GECCO '13 Companion, 2013

ABSTRACT In this paper, a GPU-based implementation of Differential Evolution (DE) and Particle Sw... more ABSTRACT In this paper, a GPU-based implementation of Differential Evolution (DE) and Particle Swarm Optimization (PSO) in CUDA is used to automatically tune the parameters of PSO. The parameters were tuned over a set of 8 problems and then tested over 20 problems to assess the generalization ability of the tuners. We compare the results obtained using such parameters with the &#39;standard&#39; ones proposed in the literature and the ones obtained by state-of-the-art tuning methods (irace). The results are comparable to the ones suggested for the standard version of PSO (SPSO), and the ones obtained by irace, while the GPU implementation makes tuning time acceptable. To the best of our knowledge, this is the first time that a general purpose library of GPU-based metaheuristics is used to solve this problem, as well as being one of the few cases where DE and PSO are both used as tuners.

libCudaOptimize

Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12, 2012

Evolutionary Computation techniques and other metaheuristics have been increasingly used in the l... more Evolutionary Computation techniques and other metaheuristics have been increasingly used in the last years for solving many real-world tasks that can be formulated as optimization problems. Among their numerous strengths, a major one is their natural predisposition to parallelization. In this paper, we introduce libCudaOptimize, an open source library which implements some metaheuristics for continuous optimization: presently Particle Swarm Optimization, Differential Evolution, Scatter Search, and Solis&amp;Wets local search. This library allows users either to apply these metaheuristics directly to their own fitness function or to extend it by implementing their own parallel optimization techniques. The library is written in CUDA-C to make extensive use of parallelization, as allowed by Graphics Processing Units. After describing the library, we consider two practical case studies: the optimization of a fitness function for the automatic localization of anatomical brain structures in histological images, and the parallel implementation of Simulated Annealing as a new module, which extends the library while keeping code compatibility with it, so that the new method can be readily available for future use within the library as an alternative optimization technique.

Texture Classification of Proteins Using Support Vector Machines and Bio-inspired Metaheuristics

Communications in Computer and Information Science, 2014

ABSTRACT In this paper, a novel classification method of two-dimensional polyacrylamide gel elect... more ABSTRACT In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process.

Diagnóstico por Imagen en Reumatología: de la Imagen Radiológica a la Imagen Molecular

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Artificial Neuron-Glia Networks Learning Approach Based on Cooperative Coevolution

International Journal of Neural Systems, 2015

Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They ... more Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

An experimental study on the automatic segmentation of in situ hybridization-derived images

Histology is a well established branch of biology focused on the study of the microscopic anatomy... more Histology is a well established branch of biology focused on the study of the microscopic anatomy of cells and tissues. Despite its importance, it is surprising to check that most literature devoted to histological image processing and analysis is focused on registration and 3D reconstruction of the whole brain. Therefore, there is a lack of research about automatic segmentation of anatomical brain structures in histological images. To bridge this gap, this paper introduces a comparative study of different segmentation techniques applied to this kind of images.

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2D-page texture classification using support vector machines and genetic algorithms an hybrid approach for texture image analysis

ABSTRACT In this paper, a novel texture classification method from two-dimensional electrophoresi... more ABSTRACT In this paper, a novel texture classification method from two-dimensional electrophoresis gel images is presented. Such a method makes use of textural features that are reduced to a more compact and efficient subset of characteristics by means of a Genetic Algorithm-based feature selection technique. Then, the selected features are used as inputs for a classifier, in this case a Support Vector Machine. The accuracy of the proposed method is around 94%, and has shown to yield statistically better performances than the classification based on the entire feature set. We found that the most decisive and representative features for the textural classification of proteins are those related to the second order co-occurrence matrix. This classification step can be very useful in order to discard over-segmented areas after a protein segmentation or identification process.

Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS), 2012

Abstract We perform a two-step segmentation of the hippocampus in histological images. First, we ... more Abstract We perform a two-step segmentation of the hippocampus in histological images. First, we maximize the overlap of an empirically-derived parametric Deformable Model with two crucial landmark sub-structures in the brain image using Differential Evolution. Then, the points located in the previous step determine the region where a thresholding technique based on Otsu&amp;amp;amp;amp;amp;#x27;s method is to be applied. Finally, the segmentation is expanded employing Random Forest in the regions not covered by the model. Our approach showed an ...

Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC

PLoS ONE, 2013

Motivation: RNA molecules specifically enriched in the neuropil of neuronal cells and in particul... more Motivation: RNA molecules specifically enriched in the neuropil of neuronal cells and in particular in dendritic spines are of great interest for neurobiology in virtue of their involvement in synaptic structure and plasticity. The systematic recognition of such molecules is therefore a very important task. High resolution images of RNA in situ hybridization experiments contained in the Allen Brain Atlas (ABA) represent a very rich resource to identify them and have been so far exploited for this task through human-expert analysis. However, software tools that may automatically address the same objective are not very well developed.

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Artificial Astrocytes Improve Neural Network Performance

PLoS ONE, 2011

Compelling evidence indicates the existence of bidirectional communication between astrocytes and... more Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

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Automatic hippocampus localization in histological images using Differential Evolution-based deformable models

Pattern Recognition Letters, 2013

In this paper, the localization of structures in biomedical images is considered as a multimodal ... more In this paper, the localization of structures in biomedical images is considered as a multimodal global continuous optimization problem and solved by means of soft computing techniques. We have developed an automatic method aimed at localizing the hippocampus in histological images, after discoveries indicating the relevance of structural changes of this region as early biomarkers for Alzheimer&amp;#x27;s disease and epilepsy. The localization is achieved by searching the parameters of an empirically-derived deformable model of the ...

Optimización mediante Computación Evolutiva de la interacción Neurona-Astrocito en Redes Neurogliales Artificiales

Resumen--Las Redes NeuroGliales Artificiales son sistemas conexionistas que incorporan, además de... more Resumen--Las Redes NeuroGliales Artificiales son sistemas conexionistas que incorporan, además de neuronas artificiales, elementos de control que simulan el comportamiento de los astrocitos cerebrales (células del Sistema Glial). Tras descubrirse, mediante experimentos en laboratorios de Neurociencia, que los astrocitos participan de modo activo en el procesamiento de la información en el cerebro, se ha demostrado recientemente a través de técnicas de Inteligencia Artificial, que incorporando astrocitos artificiales a ...

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