Abstract
Image classification has wide applications in many fields including medical imaging. A major aspect of classification is to extract features that can correctly represent important variations in an image. Global image features commonly used for classification include Intensity Histograms, Haralick’s features based on Gray-level co-occurrence matrix, Local Binary Patterns and Gabor filters. A novel feature extraction and image representation technique ‘Pixel N-grams’ inspired from ‘Character N-grams’ concept in text categorization is described in this chapter. The classification performance of Pixel N-grams is tested on the various datasets including UIUC texture dataset, binary shapes dataset, miniMIAS dataset of mammography, and real-world high-resolution mammography dataset provided by an Australian radiology practice. The results are compared with other feature extraction techniques such as co-occurrence matrix features, intensity histogram, and bag of visual words. The results demonstrate promising classification accuracy in addition to reduced computational costs, enabling a new way for mammographic classification on low resource computers.
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Notes
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Truly digital mammograms/primary digital mammograms are digital mammograms directly generated with the help of advanced imaging equipment.
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Secondary digital mammograms are conventional film-based mammograms digitised with the help of a scanner.
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MATLAB 7.9.0.
References
Abdel-Zaher, A.M., Eldeib, A.M.: Breast cancer classification using deep belief networks. Expert Syst. Appl. 46, 139–144 (2016)
Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.G.: Convolutional neural networks for mammography mass lesion classification. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 797–800. IEEE (2015)
Bankman, I.: Handbook of Medical Image Processing and Analysis. Academic Press (2008)
Cheng, E., Xie, N., Ling, H., Bakic, P.R., Maidment, A.D., Megalooikonomou, V.: Mammographic image classification using histogram intersection. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 197–200. IEEE (2010)
Dhungel, N., Carneiro, G., Bradley, A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2015)
Eberl, M.M., Fox, C.H., Edge, S.B., Carter, C.A., Mahoney, M.C.: BI-RADS classification for management of abnormal mammograms. J. Am. Board Family Med. 19, 161–164 (2006)
El-Faramawy, N., Rangayyan, R., Desautels, J., Alim, O.: Shape factors for analysis of breast tumors in mammograms. In: 1996 Canadian Conference on Electrical and Computer Engineering, pp. 355–358. IEEE (1996)
Engeland, S.V.: Detection of Mass Lesions in Mammograms by Using Multiple Views, [Sl: sn] (2006)
Evans, K.K., Birdwell, R.L., Wolfe, J.M.: If you don’t find it often, you often don’t find it: why some cancers are missed in breast cancer screening. PLoS ONE 8, e64366 (2013)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. In: IEEE Transactions on Systems, Man and Cybernetics, pp. 610–621 (1973)
Huo, Z., Giger, M.L., Vyborny, C.J., Wolverton, D.E., Schmidt, R.A., Doi, K.: Automated computerized classification of malignant and benign masses on digitized mammograms. Acad. Radiol. 5, 155–168 (1998)
Hussain, M., Khan, S., Muhammad, G., Berbar, M., Bebis, G.: Mass detection in digital mammograms using gabor filter bank. In: IET Conference on Image Processing (IPR 2012), pp. 1–5. IET (2012)
Islam, M.J., Ahmadi, M., Sid-Ahmed, M.A.: An efficient automatic mass classification method in digitized mammograms using artificial neural network. arXiv preprint arXiv:1007.5129 (2010)
Jalalian, A., Mashohor, S.B., Mahmud, H.R., Saripan, M.I.B., Ramli, A.R.B., Karasfi, B.: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin. Imaging 37, 420–426 (2013)
Jirari, M.: Computer Aided System For Detecting Masses in Mammograms. Kent State University (2008)
Joseph, S., Balakrishnan, K.: Local binary patterns, haar wavelet features and haralick texture features for mammogram image classification using artificial neural networks. In: Advances in Computing and Information Technology. Springer, Berlin (2011)
Kulkami, P., Stranieri, A., Ugon, J.: Texture image classification using pixel N-grams. In: IEEE International Conference on Signal and Image Processing (ICSIP), pp. 137–141. IEEE (2016)
Kulkarni, P., Stranieri, A.: Comparison of pixel N-grams with histogram, haralick’s features and bag-of-visual-words for texture image classification. In: 2018 3rd International Conference for Convergence in Technology (I2CT), pp. 1–5. IEEE (2018)
Kulkarni, P., Stranieri, A., Kulkarni, S., Ugon, J., Mittal, M.: Visual character n-grams for classification and retrieval of radiological images. Int. J. Multimed. Appl. 6, 35 (2014)
Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1265–1278 (2005)
Li, Y., Chen, H., Rohde, G.K., Yao, C., Cheng, L.: Texton analysis for mass classification in mammograms. Pattern Recogn. Lett. 52, 87–93 (2015)
Lladó, X., Oliver, A., Freixenet, J., Martí, R., Martí, J.: A textural approach for mass false positive reduction in mammography. Comput. Med. Imaging Graph. 33, 415–422 (2009)
Lu, S., Bottema, M.J.: Structural image texture and early detection of breast cancer. In: Proceedings of the 2003 APRS Workshop on Digital Image Computing, pp. 15–20 (2003)
Materka, A., Strzelecki, M.: Texture analysis methods—a review. Technical University of Lodz, Institute of Electronics, COST B11 Report, Brussels, pp. 9–11 (1998)
Mckenzie, E.: Breast Cancer Screening (2014)
Mousa, R., Munib, Q., Moussa, A.: Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert Syst. Appl. 28, 713–723 (2005)
Mu, T., Nandi, A.K., Rangayyan, R.M.: Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers. J. Digit. Imaging 21, 153–169 (2008)
Myatt, G.J.: Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. Wiley (2007)
Nanni, L., Brahnam, S., Lumini, A.: A very high performing system to discriminate tissues in mammograms as benign and malignant. Expert Syst. Appl. 39, 1968–1971 (2012)
Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: International Conference on Image and Signal Processing, pp. 236–243. Springer, Berlin (2008)
Papakostas, G.A., Boutalis, Y.S., Karras, D.A., Mertzios, B.G.: A new class of Zernike moments for computer vision applications. Inf. Sci. 177, 2802–2819 (2007)
Petrick, N., Chan, H.P., Sahiner, B., Helvie, M.A.: Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. Med. Phys. 26, 1642–1654 (1999)
Sivic, J., Zisserman, A.: Video Google: A Text Retrieval Approach to Object Matching in Videos. USA (2003)
te Brake, G.M., Karssemeijer, N., Hendriks, J.: Automated detection of breast carcinomas not detected in a screening program. Radiology 207, 465–471 (1998)
Tsai, C.-F.: Bag-of-words representation in image annotation: a review. ISRN Artificial Intell. 2012, 1–19 (2012)
Varela, C., Timp, S., Karssemeijer, N.: Use of border information in the classification of mammographic masses. Phys. Med. Biol. 51, 425 (2006)
Wei, C.-H., Chen, S.Y., Liu, X.: Mammogram retrieval on similar mass lesions. Comput. Methods Progr. Biomed. 106, 234–248 (2012)
Wei, C.-H., Li, Y., Huang, P.J.: Mammogram retrieval through machine learning within BI-RADS standards. J. Biomed. Inform. 44, 607–614 (2011)
Weka, W.: 3: Data Mining Software in Java. University of Waikato, Hamilton, New Zealand (www.cs.waikato.ac.nz/ml/weka) (2011)
WHO.: Latest World Cancer Statistics, International Agency for Research on Cancer (IARC) [Online]. World Health Organisation. [Accessed] (2016)
Yang, M., Kpalma, K., Ronsin, J.: A survey of shape feature extraction techniques. Pattern Recogn. 43–90 (2008)
Zhang, J., Tan, T.: Brief review of invariant texture analysis methods. Pattern Recogn. 35, 735–747 (2002)
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Kulkarni, P., Stranieri, A. (2020). Pixel N-Grams Representation for Medical Image Classification. In: Bhattacharyya, S., Konar, D., Platos, J., Kar, C., Sharma, K. (eds) Hybrid Machine Intelligence for Medical Image Analysis. Studies in Computational Intelligence, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-8930-6_2
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