Czasopismo
Tytuł artykułu
Warianty tytułu
Języki publikacji
Abstrakty
Breast cancer is one from various diseases that has got great attention in the last decades. This due to the number of women who died because of this disease. Segmentation is always an important step in developing a CAD system. This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms. This method is based on the data acquisition protocol parameter (the distance from the patient to the camera) and the image statistics of DMR-IR database. To evaluated the results of this method, an approach for the detection of breast abnormalities of thermograms was also proposed. Statistical and texture features from the segmented ROI were extracted and the SVM with its kernel function was used to detect the normal and abnormal breasts based on these features. The experimental results, using the benchmark database, DMR-IR, shown that the classification accuracy reached (100%). Also, using the measurements of the recall and the precision, the classification results reached 100%. This means that the proposed segmentation method is a promising technique for extracting the ROI of breast thermograms.(original abstract)
Słowa kluczowe
Rocznik
Tom
Strony
255--261
Opis fizyczny
Twórcy
autor
- Faculty of Computers and Information,Minia University, Egypt
autor
- Faculty of Computers and Information, Cairo University, Egypt
autor
- Faculty of Computers and Informatics, Suez Canal University, Egypt; IT4Innovations, VSB-TU of Ostrava, Czech Republic
autor
- Faculty of Computers and Information, Cairo University, Egypt
autor
- FEECS, Department of Computer Science and IT4Innovations, VSB-TU of Ostrava, Czech Republic
autor
- Department of Computer Science, Fluminense Federal University, Brazil
Bibliografia
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- S. Suganthi and S. Ramakrishnan, "Semi automatic segmentation of breast thermograms using variational level set method," in The 15th International Conference on Biomedical Engineering. Springer, 2014, pp. 231-234.
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- A. Tharwat, T. Gaber, A. E. Hassanien, M. Shahin, and B. Refaat, "Siftbased arabic sign language recognition system," vol. 334, pp. 359-370, 2015.
- N. A. Semary, A. Tharwat, E. Elhariri, and A. E. Hassanien, "Fruitbased tomato grading system using features fusion and support vector machine," in Intelligent Systems' 2014. Springer, 2015, pp. 401-410.
- M. Etehadtavakol, E. Ng, V. Chandran, and H. Rabbani, "Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms," Infrared Physics & Technology, vol. 61, pp. 274-286, 2013.
Typ dokumentu
Bibliografia
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bwmeta1.element.ekon-element-000171419502