Abstract
Breast cancer is a leading killer disease among women of the new era. As per the GLOBOCAN project, the breast cancer incidences showed an increase from 22.2 to 27% globally from 2008 to 2012. Many times, no obvious symptoms were identified in breast cancer patients. Accurate detection of breast cancer at the earliest stage is very much essential to reduce mortality. Mammography has been used as a gold standard for over 40 years in diagnosing breast diseases. Computer-Aided Detection (CAD) systems have been developed to avoid the subjective analysis of screening mammograms made by radiologist. Craniocaudal (CC) view and Mediolateral Oblique (MLO) view are commonly used for breast cancer detection and diagnosis. Detection accuracy of breast cancers can be improved as the number of views is increased. This work is focused to improve the detection performance by fusing Local Binary Pattern (LBP) features extracted from MLO and CC view mammograms through a hybrid feature fusion technique based on Firefly algorithm and Optimum-Path Forest classifier. Seven performance metrics such as accuracy, sensitivity, specificity, precision, F1 score, Mathews Correlation Coefficient (MCC) and Balanced Classification Rate (BCR) were used to analyse the detection performance. The proposed work shows better performance when compared to existing work in literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ferlay, J., Shin, H. R., Bray, F., Forman, D., Mathers, C., & Parkin, D. M. (2010). Estimates of worldwide burden of cancer in 2008 GLOBOCAN 2008. International Journal of Cancer, 127(12), 2893–2917.
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., & Bray, F. (2015). Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer, 136(5).
Takiar, R., Nadayil, D., & Nandakumar, A. (2010). Projections of number of cancer cases in India (2010–2020) by cancer groups. Asian Pacific Journal of Cancer Prevention, 11(4), 1045–1049.
De Souza Jacomini, R., do Nascimento, M. Z., Dantas, R. D., & Ramos, R. P. (2012). Comparison of PCA and ANOVA for information selection of CC and MLO views in classification of mammograms. In Proceedings of international conference on intelligent data engineering and automated learning (pp. 117–126). Berlin: Springer.
Jalalian, A., Mashohor, S., Mahmud, R., Karasfi, B., Saripan, M. I. B., & Ramli, A. R. B. (2017). Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI Journal, 16, 113.
Hossain, S., & Serikawa, S. (2012). Features for texture analysis. In Proceedings of IEEE SICE annual conference (SICE) (pp. 1739–1744).
Tourassi, G. D. (1999). Journey toward computer-aided diagnosis: role of image texture analysis. Radiology, 213(2), 317–320.
Karahaliou, A. N., Arikidis, N. S., Skiadopoulos, S. G., Panayiotakis, G. S., & Costaridou, L. I. (2012). Computerized image analysis of mammographic micro calcifications: Diagnosis and prognosis In Mammography-recent advances. InTech.
Bassett, L. W., Bunnell, D. H., Jahanshahi, R., Gold, R. H., Arndt, R. D., & Linsman, J. (1987). Breast cancer detection: One versus two views. Radiology, 165(1), 95–97.
Chakraborty, S., Dey, N., Samanta, S., Ashour, A. S., & Balas, V. E. (2016). Firefly algorithm for optimized nonrigid demons registration. In Bio-inspired computation and applications in image processing (pp. 221–237). Amsterdam: Academic.
Tang, R., Fong, S., & Dey, N. (2018). Metaheuristics and chaos theory. In Chaos theory. InTech.
Zhang, L., Liu, L., Yang, X. S., & Dai, Y. (2016). A novel hybrid firefly algorithm for global optimization. PloS one, 11(9), e0163230. Yang, X. S. (2008). Nature-inspired metaheuristic algorithms. Luniver Press. ISBN 1-905986-10-6.
Pal, S. K., Rai, C. S., & Singh, A. P. (2012). Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. International Journal of Intelligent Systems and Applications, 4, 50.
Singh, G. P., & Singh, A. (2014). Comparative study of Krill Herd, firefly and cuckoo search algorithms for unimodal and multimodal optimization. International Journal of Intelligent Systems and Applications, 6, 35.
Nakamura, R. Y., Pereira, L. A., Costa, K. A., Rodrigues, D., Papa, J. P., & Yang, X. S. (2012, August). BBA: A binary bat algorithm for feature selection. In 2012 25th SIBGRAPI conference on graphics, patterns and images (pp. 291–297). IEEE.
Papa, J. P., Falcao, A. X., & Suzuki, C. T. (2009). Supervised pattern classification based on optimum path forest. International Journal of Imaging Systems and Technology, 19(2), 120–131.
Young, K. C., Wallis, M. G., Blanks, R. G., & Moss, S. M. (1997). Influence of number of views and mammographic film density on the detection of invasive cancers: Results from the NHS breast screening programme. The British Journal of Radiology, 70(833), 482–488.
Altrichter, M., Ludányi, Z., & Horváth, G. (2005). Joint analysis of multiple mammographic views in CAD systems for breast cancer detection. In Scandinavian conference on image analysis (pp. 760–769). Berlin: Springer.
Paquerault, S., Petrick, N., Chan, H. P., Sahiner, B., & Helvie, M. A. (2002). Improvement of computerized mass detection on mammograms: Fusion of two-view information. Medical Physics, 29(2), 238–247.
Kim, S. J., Moon, W. K., Cho, N., Cha, J. H., Kim, S. M., & Im, J. G. (2006). Computer-aided detection in digital Mammography: Comparison of craniocaudal, mediolateral oblique, and mediolateral views. Radiology, 241(3), 695–701.
Gupta, S., Zhang, D., Sampat, M. P., & Markey, M. K. (2006). Combining texture features from the MLO and CC views for mammographic CADx. Progress in Biomedical Optics and Imaging, 7(3).
Magro, R., Cascio, D., Fauci, F., Presti, L. L., Raso, G., Ienzi., R., & Sorce, S. (2008). A method to reduce the FP/imm number through CC and MLO views comparison in mammographic images. In Proceedings of IEEE symposium conference on nuclear science record (pp. 4364–4367).
Velikova, M., Samulski, M., Lucas, P. J., & Karssemeijer, N. (2009). Improved mammographic CAD performance using multi-view information: A Bayesian network framework. Physics in Medicine and Biology, 54(5), 1131–1147.
Zhang, S., Chen, Z., Gu, S., Qiu, X., Qu, Q., & Wang, Z. (2013). Breast tumour detection in double views Mammography based on simple bias. In Proceedings of 2013 IEEE international conference on medical imaging physics and engineering (ICMIPE) (pp. 240–244).
Liu, X., & Zeng, Z. (2015). A new automatic mass detection method for breast cancer with false positive reduction. Neurocomputing, 152, 388–402.
Sun, L., Li, L., Xu, W., Liu, W., Zhang, J., & Shao, G. (2010). A novel classification scheme for breast masses based on multi-view information fusion. In Proceedings of 4th IEEE international conference on bioinformatics and biomedical engineering (iCBBE) (pp. 1–4).
Kim, D. H., Choi, J. Y., & Ro, Y. M. (2013). Boosting framework for mammographic mass classification with combination of CC and MLO view information. In SPIE medical imaging, international society for optics and photonics (pp. 86701V–86701V).
Sasikala, S., Ezhilarasi, M., & Rasheedha, A. (2015). Breast cancer diagnosis using texture features from both MLO & CC view mammograms. International Journal of Applied Engineering Research, 10(37), 27934–27939.
Sasikala, S., & Ezhilarasi, M. (2018). Comparative analysis of serial and parallel fusion on texture features for improved breast cancer diagnosis. Current Medical Imaging Reviews, 14, 957–968.
Virmani, J. (2016). Comparison of CAD systems for three class breast tissue density classification using mammographic images. In Medical imaging in clinical applications (pp. 107–130). Cham: Springer.
Bhattacherjee, A., Roy, S., Paul, S., Roy, P., Kausar, N., & Dey, N. (2016). Classification approach for breast cancer detection using back propagation neural network: a study. In Biomedical image analysis and mining techniques for improved health outcomes (pp. 210–221). IGI Global.
Cheriguene, S., Azizi, N., Zemmal, N., Dey, N., Djellali, H., & Farah, N. (2016). Optimized tumor breast cancer classification using combining random subspace and static classifiers selection paradigms. In Applications of intelligent optimization in biology and medicine (pp. 289–307). Cham: Springer.
Virmani, J., Dey, N., & Kumar, V. (2016). PCA-PNN and PCA-SVM based CAD systems for breast density classification. In Applications of intelligent optimization in biology and medicine (pp. 159–180). Cham: Springer.
Zemmal, N., Azizi, N., Dey, N., & Sellami, M. (2016). Adaptive semi supervised support vector machine semi supervised learning with features cooperation for breast cancer classification. Journal of Medical Imaging and Health Informatics, 6(1), 53–62.
Sasikala, S., Bharathi, M., Ezhilarasi, M., Ramasubba Reddy, M., Arunkumar, S. (2018). Fusion of MLO and CC view binary patterns to improve the performance of breast cancer diagnosis. Current Medical Imaging Reviews, 14, 651–658.
Blanks, R. G., Wallis, M. G., & Given-Wilson, R. M. (1999). Observer variability in cancer detection during routine repeat (incident) mammographic screening in a study of two versus one view Mammography. Journal of Medical Screening, 6(3), 152–158.
Sahiner, B., Chan, H. P., Hadjiiski, L. M., Helvie, M. A., Paramagul, C., Ge, J., et al. (2006). Joint two‐view information for computerized detection of micro calcifications on Mammograms. Medical Physics, 33(7), 2574–2585.
Dantas, R. D., do Nascimento, M. Z., de Souza Jacomini, R., Pereira, D. C., & Ramos, R. P. (2012). Fusion of two-view information: SVD based modeling for computerized classification of breast lesions on Mammograms. In Mammography-recent advances. InTech., pp. 261–278.
Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P., Moore, R., Chang, K., et al. (1998). Current status of the digital database for screening Mammography. Digital Mammography (pp. 457–460). Netherlands: Springer.
Moreira, I. C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M. J., & Cardoso, J. S. (2012). Inbreast: Toward a full-field digital mammographic database. Academic Radiology, 19(2), 236–248.
Li, B. N., Chui, C. K., Chang, S., & Ong, S. H. (2011). Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine, 41(1), 1–10.
Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1), 51–59.
Wang, Z., Qu, Q., Yu, G., & Kang, Y. (2016). Breast tumor detection in double views mammography based on extreme learning machine. Neural Computing and Applications, 27(1), 227–240.
Mangai, U. G., Samanta, S., Das, S., & Chowdhury, P. R. (2010). A survey of decision fusion and feature fusion strategies for pattern classification. IETE Technical Review, 27(4), 293–307.
Diniz, W. F., Fremont, V., Fantoni, I., & Nóbrega, E. G. (2015). Evaluation of optimum path forest classifier for pedestrian detection. In Proceedings IEEE international conference on robotics and biomimetics (ROBIO) (pp. 899–904).
Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In Proceedings international symposium on stochastic algorithms (pp. 169–178). Berlin: Springer.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth ACM annual workshop on computational learning theory (pp. 144–152).
Sasikala, S., & Ezhilarasi, M. (2016). Combination of mammographic texture feature descriptors for improved breast cancer diagnosis. Asian Journal of Information Technology, 15(20), 4054–4062.
Sasikala, S., & Ezhilarasi, M. (2018). Fusion of k-Gabor features from medio-lateral-oblique and craniocaudal view mammograms for improved breast cancer diagnosis. Journal of Cancer Research and Therapeutics, 14(5), 1036.
Sasikala, S., Bharathi, M., Ezhilarasi, M., Ramasubba Reddy, M., & Arunkumar, S. (2018). Fusion of MLO and CC view binary patterns to improve the performance of breast cancer diagnosis. Current Medical Imaging Reviews, 14(4), 651–658.
Acknowledgements
The authors acknowledge TM Deserni, Deptartment of Medical Informatics and RWTH Achen, Germany for providing the dataset.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sasikala, S., Ezhilarasi, M., Arun Kumar, S. (2020). Detection of Breast Cancer Using Fusion of MLO and CC View Features Through a Hybrid Technique Based on Binary Firefly Algorithm and Optimum-Path Forest Classifier. In: Dey, N., Ashour, A., Bhattacharyya, S. (eds) Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-9263-4_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-9263-4_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9262-7
Online ISBN: 978-981-13-9263-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)