Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic Images
Introduction: Thermography is a non-invasive imaging technique that can be used to diagnose breast cancer. In this study, a method was presented for the extraction of suitable features in dynamic thermographic images of breast. The extracted features can help classify thermographic images as cancero...
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| Format: | Article |
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Kerman University of Medical Sciences
2020-09-01
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| Series: | مجله انفورماتیک سلامت و زیست پزشکی |
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| Online Access: | http://jhbmi.ir/article-1-381-en.html |
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| author | Hossein Ghayoumi Zadeh Ali Fayazi Bita Binazir Mostafa Yargholi |
| author_facet | Hossein Ghayoumi Zadeh Ali Fayazi Bita Binazir Mostafa Yargholi |
| author_sort | Hossein Ghayoumi Zadeh |
| collection | DOAJ |
| description | Introduction: Thermography is a non-invasive imaging technique that can be used to diagnose breast cancer. In this study, a method was presented for the extraction of suitable features in dynamic thermographic images of breast. The extracted features can help classify thermographic images as cancerous or healthy.
Method: In this descriptive-analytical study, the images were taken from the IC/UFF database. A total of 196 people, including 41 cancer patients and 155 healthy individuals were investigated. Each person had 10 thermographic images and in total, 1960 images were analyzed. The images were captured using the FLIR ThermaCam S45 camera. The proposed model was presented based on a series of breast thermographic images of an individual to extract 8 suitable features. The extracted features included mean, standard deviation, entropy, kurtosis, homogeneity, energy, skewness, and variance.
Results: The extracted features were evaluated by the classifiers including the decision tree, support vector machine, quadratic symmetric analysis, and K-nearest neighbor algorithm using the ten-fold cross validation. The accuracy and sensitivity were 99% and 99.33% for decision tree algorithm, 98.46% and 95.12% for support vector machine algorithm, 100% and 100%, and 99% and 97.56% for K-nearest neighbor algorithm.
Conclusion: The results of this study showed that among the first-order statistical features, mean difference, skewness, entropy, and standard deviation are the most effective features which help to detect asymmetry. The features extracted by the proposed model can help classify the individuals into healthy or cancer-affected by thermal images. |
| first_indexed | 2024-04-10T19:50:10Z |
| format | Article |
| id | doaj.art-fb9018ae782e4b698164b125d0398fa8 |
| institution | Directory Open Access Journal |
| issn | 2423-3870 2423-3498 |
| language | fas |
| last_indexed | 2024-04-10T19:50:10Z |
| publishDate | 2020-09-01 |
| publisher | Kerman University of Medical Sciences |
| record_format | Article |
| series | مجله انفورماتیک سلامت و زیست پزشکی |
| spelling | doaj.art-fb9018ae782e4b698164b125d0398fa82023-01-28T10:30:33ZfasKerman University of Medical Sciencesمجله انفورماتیک سلامت و زیست پزشکی2423-38702423-34982020-09-017291101Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic ImagesHossein Ghayoumi Zadeh0Ali Fayazi1Bita Binazir2Mostafa Yargholi3 Ph.D. in Biomedical Engineering, Assistant Professor, Electrical Engineering Dept., Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran Ph.D. in Control Engineering, Assistant Professor, Electrical Engineering Dept., Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran M.Sc. in Electrical Engineering, Electrical Engineering Dept., University of Zanjan, Zanjan, Iran Ph.D. in Electrical Engineering, Associate Professor, Electrical Engineering Dept., University of Zanjan, Zanjan, Iran Introduction: Thermography is a non-invasive imaging technique that can be used to diagnose breast cancer. In this study, a method was presented for the extraction of suitable features in dynamic thermographic images of breast. The extracted features can help classify thermographic images as cancerous or healthy. Method: In this descriptive-analytical study, the images were taken from the IC/UFF database. A total of 196 people, including 41 cancer patients and 155 healthy individuals were investigated. Each person had 10 thermographic images and in total, 1960 images were analyzed. The images were captured using the FLIR ThermaCam S45 camera. The proposed model was presented based on a series of breast thermographic images of an individual to extract 8 suitable features. The extracted features included mean, standard deviation, entropy, kurtosis, homogeneity, energy, skewness, and variance. Results: The extracted features were evaluated by the classifiers including the decision tree, support vector machine, quadratic symmetric analysis, and K-nearest neighbor algorithm using the ten-fold cross validation. The accuracy and sensitivity were 99% and 99.33% for decision tree algorithm, 98.46% and 95.12% for support vector machine algorithm, 100% and 100%, and 99% and 97.56% for K-nearest neighbor algorithm. Conclusion: The results of this study showed that among the first-order statistical features, mean difference, skewness, entropy, and standard deviation are the most effective features which help to detect asymmetry. The features extracted by the proposed model can help classify the individuals into healthy or cancer-affected by thermal images.http://jhbmi.ir/article-1-381-en.htmldynamic modelthermographybreast cancerfeature extraction |
| spellingShingle | Hossein Ghayoumi Zadeh Ali Fayazi Bita Binazir Mostafa Yargholi Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic Images مجله انفورماتیک سلامت و زیست پزشکی dynamic model thermography breast cancer feature extraction |
| title | Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic Images |
| title_full | Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic Images |
| title_fullStr | Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic Images |
| title_full_unstemmed | Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic Images |
| title_short | Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic Images |
| title_sort | extraction of suitable features for breast cancer detection using dynamic analysis of thermographic images |
| topic | dynamic model thermography breast cancer feature extraction |
| url | http://jhbmi.ir/article-1-381-en.html |
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