Interval symbolic feature extraction for thermography breast cancer detection |
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| Affiliation: | 1. Mechanical Engineering Department, Federal University of Pernambuco, Av. Prof. Moraes Rego, 1235, Cidade Universitária, CEP 50670-901 Recife, PE, Brazil;2. Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, CEP 50740-560 Recife, PE, Brazil;1. Department of Electronics Convergence Engineering, Wonkwang University, 344-2, Shinyong-Dong, Iksan, Jeonbuk 570-749, South Korea;2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2G7, Canada;3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2950, Valparaíso, Chile;2. Universidad Finis Terrae, Av. Pedro de Valdivia 1509, Santiago, Chile;3. Universidad de Playa Ancha, Av. Leopoldo Carvallo 270, Valparaíso, Chile;4. Universidad Autónoma de Chile, Pedro de Valdivia 641, Santiago, Chile;5. CNRS, LINA, University of Nantes, 2 rue de la Houssinière, Nantes, France;6. Escuela de Ingeniería Industrial, Universidad Diego Portales, Manuel Rodríguez Sur 415, Santiago, Chile;1. Department of Systems Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenu, Kowloon Tong, Hong Kong;2. Centre for Systems Informatics Engineering, City University of Hong Kong, 83 Tat Chee Avenu, Kowloon Tong, Hong Kong;3. School of Management, Hefei University of Technology, Hefei, Box 270, Hefei 230009, Anhui, PR China;4. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Box 270, Hefei 230009, Anhui, PR China |
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| Abstract: | Breast cancer is one of the leading causes of death in women. Recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. The aim of this work is to evaluate the feasibility of using interval data in the symbolic data analysis (SDA) framework to model breast abnormalities (malignant, benign and cyst) in order to detect breast cancer. SDA allows a more realistic description of the input units by taking into consideration their internal variation. In this direction, a three-stage feature extraction approach is proposed. In the first stage four intervals variables are obtained by the minimum and maximum temperature values from the morphological and thermal matrices. In the second one, operators based on dissimilarities for intervals are considered and then continuous features are obtained. In the last one, these continuous features are transformed by the Fisher’s criterion, giving the input data to the classification process. This three-stage approach is applied to a Brazilian’s thermography breast database and it is compared with a statistical feature extraction and a texture feature extraction approach widely used in thermal imaging studies. Different classifiers are considered to detect breast cancer, achieving 16% of misclassification rate, 85.7% of sensitivity and 86.5% of specificity to the malignant class. |
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| Keywords: | Thermography SDA Interval data Classification |
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