Detection of Breast Abnormalities of Thermograms based on a New Segmentation Method

• Autorzy: Mona A. S. Ali , Gehad Ismail Sayed , Tarek Gaber , Aboul Ella Hassanien , Vaclav Snasel , Lincoln F. Silva
• Języki publikacji: EN

Abstrakty

EN

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

EN

Medical technology; Medicine

PL

Technologia medyczna; Medycyna

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2015
• Tom: 5
• Strony: 255--261

Twórcy

autor

Mona A. S. Ali

• Faculty of Computers and Information,Minia University, Egypt

autor

Gehad Ismail Sayed

• Faculty of Computers and Information, Cairo University, Egypt

autor

Tarek Gaber

• Faculty of Computers and Informatics, Suez Canal University, Egypt; IT4Innovations, VSB-TU of Ostrava, Czech Republic

autor

Aboul Ella Hassanien

• Faculty of Computers and Information, Cairo University, Egypt

autor

Vaclav Snasel

• FEECS, Department of Computer Science and IT4Innovations, VSB-TU of Ostrava, Czech Republic

autor

Lincoln F. Silva

• Department of Computer Science, Fluminense Federal University, Brazil

Bibliografia

• R. Siegel, J. Ma, Z. Zou, and A. Jemal, "Cancer statistics, 2014," CA: a cancer journal for clinicians, vol. 64, no. 1, pp. 9-29, 2014.
• X. Yao, "A comparison of mammography, ultrasonography, and farinfrared thermography with pathological results in screening and early diagnosis of breast cancer," Asian Biomed, vol. 8, no. 1, 2014.
• T. B. Borchartt, A. Conci, R. C. Lima, R. Resmini, and A. Sanchez, "Breast thermography from an image processing viewpoint: A survey," Signal Processing, vol. 93, no. 10, pp. 2785-2803, 2013.
• L. Silva, G. Sequeiros, M. L. Santos, C. Fontes, D. C. Muchaluat- Saade, and A. Conci, "Thermal signal analysis for breast cancer risk verification," in MEDINFO'15 - 15th World Congress on International Health and Biomedical Informatics, 2015.
• N. Arora, D. Martins, D. Ruggerio, E. Tousimis, A. J. Swistel, M. P. Osborne, and R. M. Simmons, "Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer," The American Journal of Surgery, vol. 196, no. 4, pp. 523-526, 2008.
• D. Machado, G. Giraldi, A. Novotny, R. Marques, and A. Conci, "Topological derivative applied to automatic segmentation of frontal breast thermograms," 2013.
• 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.
• S. S. Srinivasan and R. Swaminathan, "Segmentation of breast tissues in infrared images using modified phase based level sets," in Biomedical Informatics and Technology. Springer, 2014, pp. 161-174.
• Q. Zhou, Z. Li, and J. K. Aggarwal, "Boundary extraction in thermal images by edge map," in Proceedings of the 2004 ACM symposium on Applied computing. ACM, 2004, pp. 254-258.
• L. F. Silva, G. O. S. Olivera, S. Galvao, J. B. Silva, A. A. S. M. D. Santos, D. C. Muchaluat-Saade, and A. Conci, "Análise de séries temporais de sinais térmicos da mama para detecção de anomalias (analysis of time series of breast thermal signs for anomaly detection)," in WIM - XIV Workshop de InformÃa˛tica MÃl'dica. Anais CSBC, 2014, pp. 1818-1827.
• U. R. Acharya, E. Y.-K. Ng, J.-H. Tan, and S. V. Sree, "Thermography based breast cancer detection using texture features and support vector machine," Journal of medical systems, vol. 36, no. 3, pp. 1503-1510, 2012.
• T. Jakubowska, B. Wiecek, M. Wysocki, C. Drews-Peszynski, and M. Strzelecki, "Classification of breast thermal images using artificial neural networks," Journal of Medical Informatics & Technologies, vol. 7, pp. 41-50, 2004.
• S. V. Francis, M. Sasikala, and S. Saranya, "Detection of breast abnormality from thermograms using curvelet transform based feature extraction," Journal of medical systems, vol. 38, no. 4, pp. 1-9, 2014.
• M. C. Araújo, R. C. Lima, and R. M. De Souza, "Interval symbolic feature extraction for thermography breast cancer detection," Expert Systems with Applications, vol. 41, no. 15, pp. 6728-6737, 2014.
• U. R. Acharya, E. Y.-K. Ng, S. V. Sree, C. K. Chua, and S. Chattopadhyay, "Higher order spectra analysis of breast thermograms for the automated identification of breast cancer," Expert Systems, vol. 31, no. 1, pp. 37-47, 2014.
• S. Suganthi and S. Ramakrishnan, "Analysis of breast thermograms using gabor wavelet anisotropy index," Journal of medical systems, vol. 38, no. 9, pp. 1-7, 2014.
• S. Prabha, K. Anandh, C. Sujatha, and S. Ramakrishnan, "Total variation based edge enhancement for level set segmentation and asymmetry analysis in breast thermograms," in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. IEEE, 2014, pp. 6438-6441.
• E. Rodrigues, A. Conci, T. Borchartt, A. Paiva, A. C. Silva, and T. MacHenry, "Comparing results of thermographic images based diagnosis for breast diseases," in Systems, Signals and Image Processing (IWSSIP), 2014 International Conference on. IEEE, 2014, pp. 39-42.
• A. Tharwat, T. Gaber, and A. E. Hassanien, "Cattle identification based on muzzle images using gabor features and svm classifier," in Advanced Machine Learning Technologies and Applications. Springer, 2014, pp. 236-247.
• L. Silva, D. Saade, G. Sequeiros, A. Silva, A. Paiva, R. Bravo, and A. Conci, "A new database for breast research with infrared image," Journal of Medical Imaging and Health Informatics, vol. 4, no. 1, pp. 92-100, 2014.
• R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural features for image classification," Systems, Man and Cybernetics, IEEE Transactions on, no. 6, pp. 610-621, 1973.
• H. Xu, C. Caramanis, and S. Mannor, "Robustness and regularization of support vector machines," The Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009.
• A. Tharwat, T. Gaber, A. E. Hassanien, H. A. Hassanien, and M. F. Tolba, "Cattle identification using muzzle print images based on texture features approach," vol. 303, pp. 217-227, 2014.
• 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.

Identyfikatory

DOI

10.15439/2015F318