S. Pérez-Buitrago, Sara Tobón-Pareja, Yeraldín Gómez-Gaviria, A. Guerrero-Peña, Gloria Díaz-Londoño
{"title":"Methodology to evaluate temperature changes in multiple sclerosis patients by calculating texture features from infrared thermography images","authors":"S. Pérez-Buitrago, Sara Tobón-Pareja, Yeraldín Gómez-Gaviria, A. Guerrero-Peña, Gloria Díaz-Londoño","doi":"10.1080/17686733.2020.1793283","DOIUrl":null,"url":null,"abstract":"ABSTRACT Multiple sclerosis (MS) is a progressive and degenerative disease that causes nerve conduction blocks due to demyelination in the central nervous system. Most MS patients experience a worsening of clinical signs and neurological symptoms when they are exposed to heat due to a thermoregulatory dysfunction. This paper proposes a novel methodology to understand temperature changes in MS patients by obtaining and evaluating texture features from infrared thermography (IRT) images. For that purpose, images of the legs of a MS patient and a healthy control subject with similar physical characteristics (while at rest and in a standing position) were recorded using a FLIR A655SC infrared camera. In the quantitative analysis of the resulting IRT images, three texture features (average, entropy, and uniformity) were computed, and the results were compared using statistical techniques. The statistical analysis showed that temperatures in the MS patient were not normally distributed, while those in the healthy control subject were normally distributed. In addition, significant differences in average, entropy, and uniformity were found between subjects. This methodology enables a quantitative evaluation of thermal distributions over different regions of the body and can be used in further studies into temperature changes in MS patients.","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"1 - 11"},"PeriodicalIF":3.7000,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2020.1793283","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Infrared Thermography Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/17686733.2020.1793283","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 5
Abstract
ABSTRACT Multiple sclerosis (MS) is a progressive and degenerative disease that causes nerve conduction blocks due to demyelination in the central nervous system. Most MS patients experience a worsening of clinical signs and neurological symptoms when they are exposed to heat due to a thermoregulatory dysfunction. This paper proposes a novel methodology to understand temperature changes in MS patients by obtaining and evaluating texture features from infrared thermography (IRT) images. For that purpose, images of the legs of a MS patient and a healthy control subject with similar physical characteristics (while at rest and in a standing position) were recorded using a FLIR A655SC infrared camera. In the quantitative analysis of the resulting IRT images, three texture features (average, entropy, and uniformity) were computed, and the results were compared using statistical techniques. The statistical analysis showed that temperatures in the MS patient were not normally distributed, while those in the healthy control subject were normally distributed. In addition, significant differences in average, entropy, and uniformity were found between subjects. This methodology enables a quantitative evaluation of thermal distributions over different regions of the body and can be used in further studies into temperature changes in MS patients.
期刊介绍:
The Quantitative InfraRed Thermography Journal (QIRT) provides a forum for industry and academia to discuss the latest developments of instrumentation, theoretical and experimental practices, data reduction, and image processing related to infrared thermography.