Christian Mata

Hospital Sant Joan de Déu Barcelona

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Elsa Pastor

Universitat Politècnica de Catalunya

Eulàlia Planas

Universitat Politècnica de Catalunya

Gilberto Ochoa‐Ruiz

Tecnológico de Monterrey

Miguel González-Mendoza

Tecnológico de Monterrey

Joan Martı́

University of Girona

Arnau Oliver

University of Girona

Mario M. Valero

San Jose State University

Alain Lalande

Université de Bourgogne

Joaquim Casal

Universitat Politècnica de Catalunya

Adriana Palacios

Universidad de las Américas Puebla

Cooperative Institutions

Universitat Politècnica de Catalunya

Tecnológico de Monterrey

University of Girona

Institut de Recerca Sant Joan de Déu

Universitat de Barcelona

Université de Bourgogne

Hospital Sant Joan de Déu Barcelona

Sant Joan de Déu Research Foundation

Rocky Mountain Research (United States)

Rocky Mountain Research Station

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A priori validation of CFD modelling of hydrocarbon pool fires

Journal of Loss Prevention in the Process Industries (2018)

Borja Rengel Christian Mata Elsa Pastor Joaquim Casal Eulàlia Planas

Domino Effect

10.1016/j.jlp.2018.08.002

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Citations (36)

Convection in a vertical channel - A finite-difference and an integral method

Numerical Methods in Laminar and Turbulent Flow (1989)

Christian Mata J. A. Gil Saraiva

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Citations (1)

Evaluation of a deep learning (DL) algorithm to detect the presence of Arteriovenous Malformations (AVM) in pediatric arterial spin labeling sequences (ASL)

Julia Romagosa Sandra Bernaus Christian Stephan‐Otto Raúl Benítez A Valls

Arterial spin labeling

Arteriovenous malformation

Intracranial Arteriovenous Malformations

10.26226/m.64ae6f4f56241620f72a77a6

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Citations (0)

Historical analysis of accidents in the Mexican chemical industry

Revista Mexicana de Ingeniería Química (2020)

Andrea Camila Manangon Palacios Christian Mata Carlos Barraza

An analysis of accidents that occurred mainly in the chemical industry within the Mexican Republic throughout the last century was made. The country's industrial growth has boosted its economic development, increasing the occurrence of high-impact accidents inside and outside chemical plants. The main purpose of this work was to create a database that contained information related to the accidents that occurred in the Mexican chemical industry. For this extensive investigation, it was necessary to obtain information from news, reports and articles related to this topic. In the database, 267 accidents were collected from 1900 until 2019. A statistical analysis showed that Petroleos Mexicanos, PEMEX, is the company with the highest accidents rate, because this company was a monopoly in the Mexican petrochemical industry until 2013. The types of accidents that occurred the most were explosions and fires, 122 and 55 of these reported accidents, respectively. The most frequently reported substances were diesel and gasoline with a 20% of the total accidents, followed by hydrocarbons and their derivatives with 18.7%. Finally, the importance of creating databases like this one is remarked, with the objective of implementing better safety measures and improving the regulations that govern the nation.

Petrochemical

Chemical industry

Statistical Analysis

10.24275/rmiq/proc1345

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Citations (4)

Texture descriptors applied to digital mammography

Christian Mata Jordi Freixenet Xavier Lladó Bardera Arnau Oliver i Malagelada

Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demon- strated an useful tool for early diagnosis, a crucial as- pect for a high survival rate. In this context, several re- search works have incorporated texture features in mam- mographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Pat- terns, and many others. This paper presents an approach for breast density classi¯cation based on segmentation and texture feature extraction techniques in order to clas- sify digital mammograms according to their internal tis- sue. The aim of this work is to compare di®erent texture descriptors on the same framework (same algorithms for segmentation and classi¯cation, as well as same images). Extensive results prove the feasibility of the proposed ap- proach.

Digital Mammography

Texture (cosmology)

Local Binary Patterns

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Citations (2)

Comparing ML based Segmentation Models on Jet Fire Radiation Zone.

arXiv (Cornell University) (2021)

Carmina Pérez-Guerrero Adriana Palacios Gilberto Ochoa‐Ruiz Christian Mata Miguel González-Mendoza

Risk assessment is relevant in any workplace, however there is a degree of unpredictability when dealing with flammable or hazardous materials so that detection of fire accidents by itself may not be enough. An example of this is the impingement of jet fires, where the heat fluxes of the flame could reach nearby equipment and dramatically increase the probability of a domino effect with catastrophic results. Because of this, the characterization of such fire accidents is important from a risk management point of view. One such characterization would be the segmentation of different radiation zones within the flame, so this paper presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem. A data set of propane jet fires is used to train and evaluate the different approaches and given the difference in the distribution of the zones and background of the images, different loss functions, that seek to alleviate data imbalance, are also explored. Additionally, different metrics are correlated to a manual ranking performed by experts to make an evaluation that closely resembles the expert's criteria. The Hausdorff Distance and Adjsted Random Index were the metrics with the highest correlation and the best results were obtained from the UNet architecture with a Weighted Cross-Entropy Loss. These results can be used in future research to extract more geometric information from the segmentation masks or could even be implemented on other types of fire accidents.

Flammable liquid

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On the Use of XML in Medical Imaging Web-Based Applications

IRBM (2016)

Christian Mata Arnau Oliver Alain Lalande Paul C. Walker Joan Martı́

SOAP

10.1016/j.irbm.2016.10.001

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Citations (5)

Management of an infrared imaging system for wildfire monitoring

Imprensa da Universidade de Coimbra eBooks (2018)

Christian Mata et. al.

In this work, we present our system architecture developed to manage and pre-and post-process TIR (thermal infrared) imagery for wildfire applications.It is mainly composed of an acquisition module, a preprocessing unit and a GUI graphical interface for post-processing analysis.Specific data-acquisition software was developed to overcome the limitations of the applications provided by camera manufacturers.This data acquisition module was complemented with a pre-processing unit to deal with those videos already recorded using manufacturers' software.This pre-processing unit converts stored IR raw files (which are unreadable) into a standard format that allows an overall thermal analysis.Our system can handle video formats used by two of the most important IR camera providers, i.e.Optris and FLIR.Finally, a graphical interface application provides the user with a user-friendly menu to detect fire edge position at desired time steps (i.e.automated perimeter tracking), to calculate rate of spread maps and interact with the data-driven forecasting module.This system makes two important contributions.First, it constitutes a common framework that ensures coherence in the treatment of IR data, no matter how it was obtained.Additionally, it incorporates the image processing algorithms in which we have been working for the past years, making them easy-to-use and facilitating the analysis of their results.

Graphical user interface

Interface (matter)

Data Processing

10.14195/978-989-26-16-506_184

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Lets Chat: Leveraging Chatbot Outreach for Improved Course Performance

AEA Randomized Controlled Trials (2024)

Katharine Meyer Lindsay C. Page Christian Mata

Chatbot

Course (navigation)

Outreach

10.1257/rct.14833

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Citations (1)

A Prostate MRI Segmentation Tool Based on Active Contour Models Using a Gradient Vector Flow

Applied Sciences (2020)

J.J. Rodriguez Gilberto Ochoa‐Ruiz Christian Mata

Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling the radiologists to use novel and easy to use semi-automatic segmentation techniques instead of manual segmentation. In this work, a detailed specification of the proposed segmentation algorithm using an Active Contour Models (ACM) aided with a Gradient Vector Flow (GVF) component is defined. The purpose is to help the manual segmentation process of the main ROIs of prostate gland zones. Finally, an experimental case of use and a discussion part of the results are presented.

Vector flow

Region of interest

Active contour model

Component (thermodynamics)

10.3390/app10186163

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Citations (5)