A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography
Breast cancer has been the second leading cause of cancer death among women. New techniques to enhance early diagnosis are very important to improve cure rates. This paper proposes and evaluates an image analysis method to automatically detect patients with breast benign and malignant changes (tumor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/14/3866 |
| _version_ | 1797562772608778240 |
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| author | Thiago Alves Elias da Silva Lincoln Faria da Silva Débora Christina Muchaluat-Saade Aura Conci |
| author_facet | Thiago Alves Elias da Silva Lincoln Faria da Silva Débora Christina Muchaluat-Saade Aura Conci |
| author_sort | Thiago Alves Elias da Silva |
| collection | DOAJ |
| description | Breast cancer has been the second leading cause of cancer death among women. New techniques to enhance early diagnosis are very important to improve cure rates. This paper proposes and evaluates an image analysis method to automatically detect patients with breast benign and malignant changes (tumors). Such method explores the difference of Dynamic Infrared Thermography (DIT) patterns observed in patients’ skin. After obtaining the sequential DIT images of each patient, their temperature arrays are computed and new images in gray scale are generated. Then the regions of interest (ROIs) of those images are segmented and, from them, arrays of the ROI temperature are computed. Features are extracted from the arrays, such as the ones based on statistical, clustering, histogram comparison, fractal geometry, diversity indices and spatial statistics. Time series that are broken down into subsets of different cardinalities are generated from such features. Automatic feature selection methods are applied and used in the Support Vector Machine (SVM) classifier. In our tests, using a dataset of 68 images, 100% accuracy was achieved. |
| first_indexed | 2024-03-10T18:32:59Z |
| format | Article |
| id | doaj.art-d663501b18eb4640b1014eec79a1a8e1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:32:59Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d663501b18eb4640b1014eec79a1a8e12023-11-20T06:27:47ZengMDPI AGSensors1424-82202020-07-012014386610.3390/s20143866A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic ThermographyThiago Alves Elias da Silva0Lincoln Faria da Silva1Débora Christina Muchaluat-Saade2Aura Conci3Federal Institute of Piauí, Teresina 64000-040, BrazilInstitute of Computing, Fluminense Federal University, Niterói, Rio de Janeiro 24220-900, BrazilInstitute of Computing, Fluminense Federal University, Niterói, Rio de Janeiro 24220-900, BrazilInstitute of Computing, Fluminense Federal University, Niterói, Rio de Janeiro 24220-900, BrazilBreast cancer has been the second leading cause of cancer death among women. New techniques to enhance early diagnosis are very important to improve cure rates. This paper proposes and evaluates an image analysis method to automatically detect patients with breast benign and malignant changes (tumors). Such method explores the difference of Dynamic Infrared Thermography (DIT) patterns observed in patients’ skin. After obtaining the sequential DIT images of each patient, their temperature arrays are computed and new images in gray scale are generated. Then the regions of interest (ROIs) of those images are segmented and, from them, arrays of the ROI temperature are computed. Features are extracted from the arrays, such as the ones based on statistical, clustering, histogram comparison, fractal geometry, diversity indices and spatial statistics. Time series that are broken down into subsets of different cardinalities are generated from such features. Automatic feature selection methods are applied and used in the Support Vector Machine (SVM) classifier. In our tests, using a dataset of 68 images, 100% accuracy was achieved.https://www.mdpi.com/1424-8220/20/14/3866breast diseasedynamic infrared thermographycancer screeningtumor diagnosis |
| spellingShingle | Thiago Alves Elias da Silva Lincoln Faria da Silva Débora Christina Muchaluat-Saade Aura Conci A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography Sensors breast disease dynamic infrared thermography cancer screening tumor diagnosis |
| title | A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography |
| title_full | A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography |
| title_fullStr | A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography |
| title_full_unstemmed | A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography |
| title_short | A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography |
| title_sort | computational method to assist the diagnosis of breast disease using dynamic thermography |
| topic | breast disease dynamic infrared thermography cancer screening tumor diagnosis |
| url | https://www.mdpi.com/1424-8220/20/14/3866 |
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