Abstract
Mammography still remains the foremost effective procedure for early diagnosis of breast cancer. To compensate with the increasing number of readings that radiologists have to undertake, Computer-aided Diagnosis (CAD) systems have become a significant assistance tool, that are used to help identify abnormal and normal regions of interest in mammograms faster and more effectively than human readers. In this paper, we propose a new approach for breast cancer identification of all type of lesions in digital mammograms by combining low- and high-level mammogram descriptors in a compact quaternionic form. Our proposed method has two major stages. Initially, a feature extraction process is proposed to extract low-level visual descriptors of Region of Suspicion (ROS), that utilizes two dimensional discrete transforms based on Angular Radial Transform (ART), Shapelets as well as textural representations based on the Haralick features. To further improve our method’s performance, the semantic information of the mammogram given by the radiologists is encoded in a 16-bit length word high-level feature vector. The features are stored in a quaternion and fused using the L2 norm prior to their presentation to the classification module. For the classification task, each ROS is recognized using a trained radial basis function neural network. The proposed method is evaluated on regions taken from the DDSM database. The proposed feature set achieves a mean accuracy of 94.22% (±1.594) and AUC = 0.9578 that is 33% higher than using only visual descriptors, showing that semantic information can improve the diagnosis when combined with standard visual features.
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References
The Global Cancer Observatory, Breast Cancer. World Heal. Organ. 876, 2018–2019 (2018)
National Cancer Institute, Cancer of the breast (female) - cancer stat facts, p. 1. SEER (2020)
World Health Organization. WHO Position Paper on Mammography Screening (2014). http://apps.who.int/iris/bitstream/10665/137339/1/9789241507936_eng.pdf?ua=1
Nickson, C., Mason, K.E., English, D.R., Kavanagh, A.M.: Mammographic screening and breast cancer mortality: a case-control study and meta-analysis. Cancer Epidemiol. Biomarkers Prev. 21, 1479–1488 (2012). https://doi.org/10.1158/1055-9965.epi-12-0468
Broeders, M., et al.: The Impact of mammographic screening on breast cancer mortality in Europe: a review of observational studies. J. Med. Screen. 19, 14–25 (2012). https://doi.org/10.1258/jms.2012.012078
Katzen, J., Dodelzon, K.: A review of computer aided detection in mammography. Clin. Imaging 52, 305–309 (2018). https://doi.org/10.1016/j.clinimag.2018.08.014
Keen, J.D., Keen, J.M., Keen, J.E.: Utilization of computer-aided detection for digital screening mammography in the United States, 2008 to 2016. J. Am. Coll. Radiol. 15, 44–48 (2018). https://doi.org/10.1016/j.jacr.2017.08.033
Kaushal, C., Bhat, S., Koundal, D., Singla, A.: Recent trends in Computer Assisted Diagnosis (CAD) system for breast cancer diagnosis using histopathological images. IRBM 40, 211–227 (2019). https://doi.org/10.1016/j.irbm.2019.06.001
Le, E.P.V., Wang, Y., Huang, Y., Hickman, S., Gilbert, F.J.: Artificial intelligence in breast imaging. Clin. Radiol. 74, 357–366 (2019). https://doi.org/10.1016/j.crad.2019.02.006
Kim, H.-E., et al.: Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit. Health. 2, 138–148 (2020). https://doi.org/10.1016/s2589-7500(20)30003-0
Yanase, J., Triantaphyllou, E.: A systematic survey of computer-aided diagnosis in medicine: past and present developments. Expert Syst. Appl. 138, 112821 (2019). https://doi.org/10.1016/j.eswa.2019.112821
Apostolopoulos, G., Koutras, A., Christoyianni, I., Dermatas, E.: Computer aided diagnosis of mammographic tissue using shapelets in quaternionic representation. In: Kyriacou, E., Christofides, S., Pattichis, C.S. (eds.) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IP, vol. 57, pp. 222–227. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32703-7_45
Apostolopoulos, G., Koutras, A., Christoyianni, I., Dermatas, E.: A new method for breast cancer identification using multi-modal features in quaternionic form. In: 2017 25th European Signal Processing Conference (EUSIPCO). IEEE (2017). https://doi.org/10.23919/eusipco.2017.8081168
Christoyianni, I., Koutras, A., Dermatas, E., Kokkinakis, G.: Computer aided diagnosis of breast cancer in digitized mammograms. Comput. Med. Imaging Graph. 26, 309–319 (2002). https://doi.org/10.1016/s0895-6111(02)00031-9
Khan, S., Hussain, M., Aboalsamh, H., Mathkour, H., Bebis, G., Zakariah, M.: Optimized gabor features for mass classification in mammography. Appl. Soft Comput. 44, 267–280 (2016). https://doi.org/10.1016/j.asoc.2016.04.012
Refregier, A.: Shapelets -- I. A method for image analysis. Mon. Not. R. Astron. Soc. 338(1), 35–47 (2003). https://doi.org/10.1046/j.1365-8711.2003.05901.x
Bober, M.: MPEG-7 visual shape descriptors. IEEE Trans. Circ. Syst. Video Technol. 11, 716–719 (2001). https://doi.org/10.1109/76.927426
MPEG-7: Mpeg-7 Visual part of experimentation Model Version 9.0. In ISO/IEC JTC1/SC29/WG11/N3914, 55th Mpeg Meeting, Pisa, January 2001
Kim, H.-K., Kim, J.-D.: Region-based shape descriptor invariant to rotation, scale and translation. Signal Process. Image Commun. 16, 87–93 (2000). https://doi.org/10.1016/s0923-5965(00)00018-7
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314
Soh, L.-K., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37, 780–795 (1999). https://doi.org/10.1109/36.752194
Slema, S., et al.: A radial basis function neural network model reference adaptive controller for nonlinear systems. In: 2018 15th International Multi-Conference on Systems, Signals and Devices (SSD), Hammamet, pp. 958–964. IEEE (2018). https://doi.org/10.1109/SSD.2018.8570538
Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Comput. 3, 246–257 (1991). https://doi.org/10.1162/neco.1991.3.2.246
Orr, M.J.L.: Regularization in the selection of radial basis function centers. Neural Comput. 7, 606–623 (1995). https://doi.org/10.1162/neco.1995.7.3.606
Heath, M., et al.: The digital database for screening mammography. In: Yaffe, M.J. (ed.) Proceedings of the Fifth International Workshop on Digital Mammography, pp. 212–218. Medical Physics Publishing (2001)
Heath, M., et al.: Current status of the digital database for screening mammography. In: Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (eds.) Digital Mammography: Nijmegen, 1998, pp. 457–460. Springer Netherlands, Dordrecht (1998). https://doi.org/10.1007/978-94-011-5318-8_75
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Apostolopoulos, G., Koutras, A., Anyfantis, D., Christoyianni, I., Dermatas, E. (2021). Improving the Diagnosis of Breast Cancer by Combining Visual and Semantic Feature Descriptors. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_7
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