Highly Discriminative Physiological Parameters for Thermal Pattern Classification

Infrared Thermography (IRT) is a non-contact, non-intrusive, and non-ionizing radiation tool used for detecting breast lesions. This paper analyzes the surface temperature distribution (STD) on an optimal Region of Interest (RoI) for extraction of suitable internal heat source parameters. The physio...

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Autores principales:	Laura Benita Alvarado-Cruz, Carina Toxqui-Quitl, Raúl Castro-Ortega, Alfonso Padilla-Vivanco, José Humberto Arroyo-Núñez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	breast thermography heat source parameters feature extraction infrared imaging D-I-R model Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/6c6452c706a84b8bb88c15911d9f5b79
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spelling	oai:doaj.org-article:6c6452c706a84b8bb88c15911d9f5b792021-11-25T18:59:01ZHighly Discriminative Physiological Parameters for Thermal Pattern Classification10.3390/s212277511424-8220https://doaj.org/article/6c6452c706a84b8bb88c15911d9f5b792021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7751https://doaj.org/toc/1424-8220Infrared Thermography (IRT) is a non-contact, non-intrusive, and non-ionizing radiation tool used for detecting breast lesions. This paper analyzes the surface temperature distribution (STD) on an optimal Region of Interest (RoI) for extraction of suitable internal heat source parameters. The physiological parameters are estimated through the inverse solution of the bio-heat equation and the STD of suspicious areas related to the hottest spots of the RoI. To reach these values, the STD is analyzed by means: the Depth-Intensity-Radius (D-I-R) measurement model and the fitting method of Lorentz curve. A highly discriminative pattern vector composed of the extracted physiological parameters is proposed to classify normal and abnormal breast thermograms. A well-defined RoI is delimited at a radial distance, determined by the Support Vector Machines (SVM). Nevertheless, this distance is less than or equal to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.8</mn></mrow></semantics></math></inline-formula> cm due to the maximum temperature location close to the boundary image. The methodology is applied to 87 breast thermograms that belong to the Database for Mastology Research with Infrared Image (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>M</mi><mi>R</mi></mrow></semantics></math></inline-formula>-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mi>R</mi></mrow></semantics></math></inline-formula>). This methodology does not apply any image enhancements or normalization of input data. At an optimal position, the three-dimensional scattergrams show a correct separation between normal and abnormal thermograms. In other cases, the feature vectors are highly correlated. According to our experimental results, the proposed pattern vector extracted at optimal position <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>a</mi><mo>=</mo><mn>1.6</mn></mrow></semantics></math></inline-formula> cm reaches the highest sensitivity, specificity, and accuracy. Even more, the proposed technique utilizes a reduced number of physiological parameters to obtain a Correct Rate Classification (CRC) of 100%. The precision assessment confirms the performance superiority of the proposed method compared with other techniques for the breast thermogram classification of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>M</mi><mi>R</mi></mrow></semantics></math></inline-formula>-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mi>R</mi></mrow></semantics></math></inline-formula>.Laura Benita Alvarado-CruzCarina Toxqui-QuitlRaúl Castro-OrtegaAlfonso Padilla-VivancoJosé Humberto Arroyo-NúñezMDPI AGarticlebreast thermographyheat source parametersfeature extractioninfrared imagingD-I-R modelChemical technologyTP1-1185ENSensors, Vol 21, Iss 7751, p 7751 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast thermography heat source parameters feature extraction infrared imaging D-I-R model Chemical technology TP1-1185
spellingShingle	breast thermography heat source parameters feature extraction infrared imaging D-I-R model Chemical technology TP1-1185 Laura Benita Alvarado-Cruz Carina Toxqui-Quitl Raúl Castro-Ortega Alfonso Padilla-Vivanco José Humberto Arroyo-Núñez Highly Discriminative Physiological Parameters for Thermal Pattern Classification
description	Infrared Thermography (IRT) is a non-contact, non-intrusive, and non-ionizing radiation tool used for detecting breast lesions. This paper analyzes the surface temperature distribution (STD) on an optimal Region of Interest (RoI) for extraction of suitable internal heat source parameters. The physiological parameters are estimated through the inverse solution of the bio-heat equation and the STD of suspicious areas related to the hottest spots of the RoI. To reach these values, the STD is analyzed by means: the Depth-Intensity-Radius (D-I-R) measurement model and the fitting method of Lorentz curve. A highly discriminative pattern vector composed of the extracted physiological parameters is proposed to classify normal and abnormal breast thermograms. A well-defined RoI is delimited at a radial distance, determined by the Support Vector Machines (SVM). Nevertheless, this distance is less than or equal to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.8</mn></mrow></semantics></math></inline-formula> cm due to the maximum temperature location close to the boundary image. The methodology is applied to 87 breast thermograms that belong to the Database for Mastology Research with Infrared Image (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>M</mi><mi>R</mi></mrow></semantics></math></inline-formula>-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mi>R</mi></mrow></semantics></math></inline-formula>). This methodology does not apply any image enhancements or normalization of input data. At an optimal position, the three-dimensional scattergrams show a correct separation between normal and abnormal thermograms. In other cases, the feature vectors are highly correlated. According to our experimental results, the proposed pattern vector extracted at optimal position <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>a</mi><mo>=</mo><mn>1.6</mn></mrow></semantics></math></inline-formula> cm reaches the highest sensitivity, specificity, and accuracy. Even more, the proposed technique utilizes a reduced number of physiological parameters to obtain a Correct Rate Classification (CRC) of 100%. The precision assessment confirms the performance superiority of the proposed method compared with other techniques for the breast thermogram classification of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>M</mi><mi>R</mi></mrow></semantics></math></inline-formula>-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mi>R</mi></mrow></semantics></math></inline-formula>.
format	article
author	Laura Benita Alvarado-Cruz Carina Toxqui-Quitl Raúl Castro-Ortega Alfonso Padilla-Vivanco José Humberto Arroyo-Núñez
author_facet	Laura Benita Alvarado-Cruz Carina Toxqui-Quitl Raúl Castro-Ortega Alfonso Padilla-Vivanco José Humberto Arroyo-Núñez
author_sort	Laura Benita Alvarado-Cruz
title	Highly Discriminative Physiological Parameters for Thermal Pattern Classification
title_short	Highly Discriminative Physiological Parameters for Thermal Pattern Classification
title_full	Highly Discriminative Physiological Parameters for Thermal Pattern Classification
title_fullStr	Highly Discriminative Physiological Parameters for Thermal Pattern Classification
title_full_unstemmed	Highly Discriminative Physiological Parameters for Thermal Pattern Classification
title_sort	highly discriminative physiological parameters for thermal pattern classification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6c6452c706a84b8bb88c15911d9f5b79
work_keys_str_mv	AT laurabenitaalvaradocruz highlydiscriminativephysiologicalparametersforthermalpatternclassification AT carinatoxquiquitl highlydiscriminativephysiologicalparametersforthermalpatternclassification AT raulcastroortega highlydiscriminativephysiologicalparametersforthermalpatternclassification AT alfonsopadillavivanco highlydiscriminativephysiologicalparametersforthermalpatternclassification AT josehumbertoarroyonunez highlydiscriminativephysiologicalparametersforthermalpatternclassification
_version_	1718410466784444416

Highly Discriminative Physiological Parameters for Thermal Pattern Classification

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