Towards a Support System for Digital Mammogram Classification

Authors

  • Adél BAJCSI Babes-Bolyai University, Faculty of Mathematics and Computer Science, 1 Mihail Kogalniceanu, Cluj-Napoca 400084, Romania Email address: adel.bajcsi@ubbcluj.ro

DOI:

https://doi.org/10.24193/subbi.2021.2.02

Keywords:

region growing, k-means, GLRLM feature extraction, GA feature selection, PCA, mammogram, classification, Decision Tree classification, Random Forest classification, MIAS.

Abstract

Cancer is the illness of the 21th century. With the development of technology some of these lesions became curable, if they are in an early stage. Researchers involved with image processing started to conduct experiments in the field of medical imaging, which contributed to the appearance of systems that can detect and/or diagnose illnesses in an early stage. This paper’s aim is to create a similar system to help the detection of breast cancer. First, the region of interest is defined using filtering and two methods, Seeded Region Growing and Sliding Window Algorithm, to remove the pectoral muscle. The region of interest is segmented using k-means and further used together with the original image. Gray-Level Run-Length Matrix features (in four direction) are extracted from the image pairs. To filter the important features from resulting set Principal Component Analysis and a genetic algorithm based feature selection is used. For classification K-Nearest Neighbor, Support Vector Machine and Decision Tree classifiers are experimented. To train and test the system images of Mammographic Image Analysis Society are used. The best performance is achieved features for directions {45◦ , 90◦ , 135◦ }, applying GA feature selection and DT classification (with a maximum depth of 30). This paper presents a comprehensive analysis of the different combinations of the algorithms mentioned above, where the best performence repored is 100% and 59.2% to train and test accuracies respectively.

Received by the editors: 22 June 2021.

2010 Mathematics Subject Classification. 68T35.

1998 CR Categories and Descriptors. I.2.1 [Artifical Intelligence]: Applications and Expert Systems – Medicine and science; I.2.6 [Artifical Intelligence]: Learning – Knowledge acquisition; I.4.6 [Image Processing and Computer Vision]: Segmentation – Pixel classification; I.4.7 [Image Processing and Computer Vision]: Feature Measurement – Feature representation;

References

Aggarwal, C. C. Data Classification: Algorithms and Applications, 1st ed. Chapman & Hall/CRC, 2014.

Arora, R., Rai, P. K., and Raman, B. Deep feature–based automatic classification of mammograms. Medical & Biological Engineering & Computing 58, 6 (June 2020), 1199–1211.

Bali, A., and Singh, S. N. A review on the strategies and techniques of image segmentation. In Proceedings of the 2015 Fifth International Conference on Advanced Computing & Communication Technologies (USA, 2015), IEEE Computer Society, p. 113–120.

Candra, D., Novitasari, R., Lubab, A., et al. Application of feature extraction for breast cancer using one order statistic, GLCM, GLRLM, and GLDM. Advances in Science, Technology and Engineering Systems Journal 4, 4 (2019), 115–120.

Chaieb, R., and Kalti, K. Feature subset selection for classification of malignant and benign breast masses in digital mammography. Pattern Analysis and Applications 22, 3 (Aug. 2019), 803–829.

Esener, I. I., Ergin, S., and Yuksel, T. A novel multistage system for the detection and removal of pectoral muscles in mammograms. Turkish Journal of Electrical Engineering and Computer Sciences 26 (2018), 35–49.

Htay, T. T., and Maung, S. S. Early stage breast cancer detection system using glcm feature extraction and k-nearest neighbor (k-nn) on mammography image. In 18th International Symposium on Communications and Information Technologies (2018), pp. 171–175.

Kamalakannan, J., and Babu, M. R. Classification of breast abnormality using decision tree based on GLCM features in mammograms. International Journal of Computer Aided Engineering and Technology 10, 5 (2018), 504–512.

Kim, W., Kanezaki, A., and Tanaka, M. Unsupervised learning of image segmentation based on differentiable feature clustering. IEEE Transactions on Image Processing 29 (2020), 8055–8068.

Li, H., Chen, D., Nailon, W. H., et al. Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography, 2020.

Liu, H., and Motoda, H. Computational Methods of Feature Selection. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, 2007.

Maitra, I. K., Nag, S., and Bandyopadhyay, S. K. Technique for preprocessing of digital mammogram. Computer Methods and Programs in Biomedicine 107, 2 (2012), 175–188.

Moghbel, M., Ooi, C. Y., Ismail, N., et al. A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography. Artificial Intelligence Review 53, 3 (Mar. 2020), 1873–1918.

Nurtanto Diaz, R. A., Nyoman Tria Swandewi, N., and Pradnyani Novianti, K. D. Malignancy determination breast cancer based on mammogram image with knearest neighbor. In 2019 1st International Conference on Cybernetics and Intelligent System (2019), vol. 1, pp. 233–237.

Rahimeto, S., Debelee, T. G., Yohannes, D., et al. Automatic pectoral muscle removal in mammograms. Evolving Systems (Nov. 2019).

Rashed, E. A., and Awad, M. G. Neural networks approach for mammography diagnosis using wavelets features, 2020.

Rouhi, R., Jafari, M., Kasaei, S., et al. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Systems with Applications 42, 3 (2015), 990–1002.

Sadeghi, B., Karimi, M., and Mazaheri, S. Automatic suspicions lesions segmentation based on variable-size windows in mammography images. Health and Technology 11, 1 (Jan. 2021), 99–110.

Salama, M. S., Eltrass, A. S., and Elkamchouchi, H. M. An improved approach for computer-aided diagnosis of breast cancer in digital mammography. In IEEE International Symposium on Medical Measurements and Applications (2018), pp. 1–5.

Sarker, O., Akter, S., and Mishu, A. A. Review on the performance of different types of filter in the presence of various noises. Engineering International 4, 2 (dec 2016), 49–56.

Saxena, A., Wang, J., and Sintunavarat, W. An empirical study on initializing centroid in k-means clustering for feature selection. International Journal of Software Science and Computational Intelligence 13, 1 (jan 2021), 1–16.

Selvathi, D., and Aarthy Poornila, A. Deep Learning Techniques for Breast Cancer Detection Using Medical Image Analysis. Springer International Publishing, Cham, 2018, pp. 159–186.

Shen, L., Margolies, L. R., Rothstein, J. H., et al. Deep learning to improve breast cancer detection on screening mammography. Scientific Reports 9, 1 (Aug. 2019).

Shinde, V., and Thirumala Rao, B. Novel approach to segment the pectoral muscle in the mammograms. In Cognitive Informatics and Soft Computing (Singapore, 2019), pp. 227–237.

Shrivastava, A., Chaudhary, A., Kulshreshtha, D., et al. Automated digital mammogram segmentation using dispersed region growing and sliding window algorithm. In 2nd International Conference on Image, Vision and Computing (June 2017), pp. 366–370.

Solorio-Fernandez, S., Carrasco-Ochoa, J. A., and Mart ´ ´ınez-Trinidad, J. F. A review of unsupervised feature selection methods. Artificial Intelligence Review 53, 2 (Feb. 2020), 907–948.

Srivastava, S., Sharma, N., Singh, S., et al. Quantitative analysis of a general framework of a CAD tool for breast cancer detection from mammograms. Journal of Medical Imaging and Health Informatics 4, 5 (Oct. 2014), 654–674.

Vijayarajeswari, R., Parthasarathy, P., Vivekanandan, S., et al. Classification of mammogram for early detection of breast cancer using SVM classifier and hough transform. Measurement 146 (2019), 800–805.

Wang, X., Liang, G., Zhang, Y., et al. Inconsistent performance of deep learning models on mammogram classification. Journal of the American College of Radiology 17, 6 (2020), 796–803.

Downloads

Published

2021-12-15

How to Cite

BAJCSI, A. (2021). Towards a Support System for Digital Mammogram Classification. Studia Universitatis Babeș-Bolyai Informatica, 66(2), 19–34. https://doi.org/10.24193/subbi.2021.2.02

Issue

Section

Articles