Abstract
Classification of benign and malignant masses in mammograms is a challenging task in the development of computer-aided diagnosis (CAD) system. Feature extraction particularly is the most important step while designing those systems as it contributes positively to the overall performance when features are discriminative. This paper presents texture-based features extraction technique that divides images into non-overlapping blocks of varying sizes prior to extraction. The images are first converted to local binary pattern (LBP) format using LBP operators, and then second-order statistics are derived from these blocks with gray level co-occurrence matrix (GLCM). Support vector machine (SVM) with radial basis function (RBF) kernel and k-nearest neighbors (KNN) were used for classification. The proposed approach was tested on complete and divided images to evaluate the performance using multiple metrics. Our results showed that our approach enhances the classification by 17, 38, 3, and 21% with respect to accuracy, sensitivity, specificity, and AUC.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rabidas, R., Midya, A., Chakraborty, J., Arif, W.: A study of different texture features based on local operator for benign-malignant mass classification. In: 6th International Conference On Advances In Computing & Communications, pp. 6–8 (2016)
Benedict, S., Williams, R.D., Hoomani, J.: Method of discovery of breast cancer. Cancer practice 4, 147–155 (1996)
Mammogram Basics: (n.d.). Retrieved from cancer: https://www.cancer.org/cancer/breast-cancer/screening-tests-and-early-detection/mammograms/mammogram-basics.html
Mammography: (n.d.). Retrieved from radiology info for patients: https://www.radiologyinfo.org/en/info.cfm?pg=mammo
Kral, P., Lenc, L.: LBP features for breast cancer detection. In: IEEE International Conference on Image Processing (ICIP) (2016)
Beura, S., Majhi, B., Dash, R.: Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 154, 1–14 (2015)
Jothilakshmi, G.R., Raaza, A.: Effective detection of mass abnormalities and its classification using multi-SVM classifier with digital mammogram images. In: IEEE International Conference on Computer, Communication and Signal Processing (ICCCSP) (2017)
Shankar, T., Ranjana, I.: Efficient approach for the classification of masses in digital mammograms. Int. J. Innov. Comput., Inf. Control 13(3), 967–978 (2017)
Zhen, H., Pei, L., Jian, M.: A novel method of extracting and classifying the features of masses in mammograms. 12th International Conference on Computer Science and Education (ICCSE), pp. 227–231 (2017)
Prakash, S.V., Ubodh, S., Rajeev, S.: Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests. Technol. Health Care 25(4), 709–727 (2017)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Ibrahim, A.M., Baharudin, B.: Classification of mammogram images using shearlet transform and kernel principal component analysis. In: 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), pp. 340–344 (2016)
Castellino, R.A.: Computer aided detection (CAD): an overview. Cancer Imaging 5(1), 17 (2005)
Obaidullah, S.M., Ahmed, S., Gonçalves, T., Rato, L.: RMID: a novel and efficient image descriptor for mammogram mass classification. In: 3rd Conference on Information Technology, Systems Research and Computational Physics, pp. 229–240 (2019)
Lee, R.S., Dunnmon, J.A., He, A., Tang, S., Ré, C., Rubin, D.L.: Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset. J. Biomed. Informat. 113, 103656 (2021)
Rabidas: Texture analysis of gradient images for benign-malignant mass classification. In: 4th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 201–205 (2017)
Tambasco, B.D., do, N. M., P., R. R., R., B. V., A., N. L., & S., M.A.: LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues. Expert Syst. Appl., 55, 329–340 (2016)
Rinku, R., Abhishek, M., Jayasree, C.: Texture analysis of gradient images for benign-malignant mass classification. In: 4th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 201–205 (2017)
DDSM: Digital Database for Screening Mammography: (n.d.). Retrieved from University of South Florida Digital Mammography Home Page: http://marathon.csee.usf.edu/Mammography/Database.html
George, M.J.: Preprocessing filters for mammogram images: a review. In: Conference on Emerging Devices and Smart Systems (ICEDSS), pp. 1–7 (2017)
Ramani, R., Vanitha, N.S., Valarmathy, S.: The pre-processing techniques for breast cancer detection in mammography images. Int. J. Image, Graph. Signal Process. 5(5), 47 (2013)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29, 51–59 (1996)
Khan, H.A., Al Helal, A., Ahmed, K.I., Mostafa, R.: Abnormal mass classification in breast mammography using rotation invariant LBP. In: 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1–5 (2016)
Timo, O., Matti, P., Topi, M.: Robust texture classification by subsets of local binary patterns. In: 15th International Conference on Pattern Recognition, 2000
Hamouchene, I., Aouat, S., Lacheheb, H.: Texture segmentation and matching using LBP operator and GLCM matrix. In: Intelligent Systems for Science and Information. Studies in Computational Intelligence, 542 (2014)
Hsu, C.-W., C.-C. C.-J.: A Practical Guide to Support Vector Classification (2016). Retrieved from https://www.csie.ntu.edu.tw/~cjlin/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kurdi, H., Alkubeyyer, A., Alabdullatif, R., Althnian, A. (2022). Classification of Mammogram Masses Using GLCM on LBP and Non-overlapping Blocks of Varying Sizes. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 287. Springer, Singapore. https://doi.org/10.1007/978-981-16-5348-3_20
Download citation
DOI: https://doi.org/10.1007/978-981-16-5348-3_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5347-6
Online ISBN: 978-981-16-5348-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)