Abstract
Mammograms are generally contaminated by quantum noise, degrading their visual quality and thereby the performance of the classifier in Computer-Aided Diagnosis (CAD). Hence, enhancement of mammograms is necessary to improve the visual quality and detectability of the anomalies present in the breasts. In this paper, a sigmoid based non-linear function has been applied for contrast enhancement of mammograms. The enhanced mammograms are used to define the texture of the detected anomaly using Gray Level Co-occurrence Matrix (GLCM) features. Later, a Back Propagation Artificial Neural Network (BP-ANN) is used as a classification tool for segregating the mammogram into abnormal or normal. The proposed classifier approach has reported to be the one with considerably better accuracy in comparison to other existing approaches.
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References
Jain, A., Singh, S., Bhateja, V.: A robust approach for denoising and enhancement of mammographic breast masses. Int. J. Converg. Comput. 1(1), 38–49 (2013) (Inderscience Publishers)
Bhateja, V., Urooj, S., Misra, M.: Technical advancements to mobile mammography using non-linear polynomial filters and IEEE 21451-1 information model. IEEE Sens. J. 15(5), 2559–2566 (2015) (Advancing Standards for Smart Transducer Interfaces)
Tang, J., Liu, X., Sun, Q.: A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms. IEEE J. Sel. Top. Signal Process. 3(1), 74–80 (2009) (IEEE)
Anitha, J., Peter, J.D.: A wavelet based morphological mass detection and classification in mammograms. In: IEEE International Conference on Machine Vision and Image Processing, pp. 25–28. IEEE (2012)
Abubaker, A.: Mass lesion detection using wavelet decomposition transform and support vector machine. Int. J. Comput. Sci. Inf. Technol. (IJCSIT), 4(2), 33–46 (2012) (IJCSIT)
Wang, H., Li, J.B., Wu, L., Gao, H.: Mammography visual enhancement in CAD-based breast cancer diagnosis. Clin. Imaging 37(2), 273–282 (2013)
Setiawan, A.S., Elysia, Wesley, J., Purnama. Y.: Mammogram classification using law’s texture energy measure and neural networks. In: International Conference on Computer Science and Computational Intelligence (ICCSCI), vol. 59, pp. 92–97 (2015)
Bhateja, V., Misra, M., Urooj, S., Lay-Ekuakille, A.: A robust polynomial filtering framework for mammographic image enhancement from biomedical sensors. IEEE Sens. J., 13(11), 4147–4156 (2013) (IEEE)
Bhateja, V., Misra, M., Urooj, S.: Non-linear polynomial filters for edge enhancement of mammogram lesions. Comput. Methods Prog. Biomed. 129C, 125–134 (2016) (Elsevier)
Bhateja, V., Misra, M., Urooj, S.: Human visual system based unsharp masking for enhancement of mammographic images. J. Comput. Sci. (2016)
Mohamed, H., Mabroukb, M.S., Sharawy, A.: Computer aided detection system for micro calcifications in digital mammograms. Comput. Methods Programs Biomed. 116(3), 226–235 (2014)
The Cancer Imaging Archive (TCIA) (2016). http://www.cancerimagingarchive.net/. Accessed 31st Aug 2016
Saini, S., Vijay, R.: Mammogram analysis using feed-forward back propagation and cascade-forward back propagation artificial neural network. In: 5th IEEE International Conference on Communication Systems and Network Technologies, pp. 1177–1180. IEEE, Gwalior (2015)
Al-Najdawia, N., Biltawib, M., Tedmorib, S.: Mammogram image visual enhancement, mass segmentation and classification. Appl. Soft Comput. 35, 175—185 (2015) (Elsevier)
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Gautam, A., Bhateja, V., Tiwari, A., Satapathy, S.C. (2018). An Improved Mammogram Classification Approach Using Back Propagation Neural Network. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542 . Springer, Singapore. https://doi.org/10.1007/978-981-10-3223-3_35
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DOI: https://doi.org/10.1007/978-981-10-3223-3_35
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