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http://dx.doi.org/10.6109/jicce.2022.20.1.58

Texture Analysis for Classifying Normal Tissue, Benign and Malignant Tumors from Breast Ultrasound Image  

Eom, Sang-Hee (Department of Early Childhood of Education, Dongju College)
Ye, Soo-Young (Department of Radiological Science, Graduate School, Catholic University of Pusan)
Publication Information
Journal of information and communication convergence engineering / v.20, no.1, 2022 , pp. 58-64 More about this Journal
Abstract
Breast ultrasonic reading is critical as a primary screening test for the early diagnosis of breast cancer. However, breast ultrasound examinations show significant differences in diagnosis based on the difference in image quality according to the ultrasonic equipment, experience, and proficiency of the examiner. Accordingly, studies are being actively conducted to analyze the texture characteristics of normal breast tissue, positive tumors, and malignant tumors using breast ultrasonography and to use them for computer-assisted diagnosis. In this study, breast ultrasonography was conducted to select 247 ultrasound images of 71 normal breast tissues, 87 fibroadenomas among benign tumors, and 89 malignant tumors. The selected images were calculated using a statistical method with 21 feature parameters extracted using the gray level co-occurrence matrix algorithm, and classified as normal breast tissue, benign tumor, and malignancy. In addition, we proposed five feature parameters that are available for computer-aided diagnosis of breast cancer classification. The average classification rate for normal breast tissue, benign tumors, and malignant tumors, using this feature parameter, was 82.8%.
Keywords
Breast cancer; Ultrasonography; Tumor; Texture analysis; Gray level co-occurrence matrix;
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1 T. Hadi, S. N. Ali, J. Gregory, and Czarnota, "Noninvasive characterization of locally advanced breast cancer using textural analysis of quantitative ultrasound parametric images," Translational Oncology, vol. 7, no. 6, pp. 759-767, 2014. DOI: 10.1016/j.tranon.2014.10.007.   DOI
2 N. Uniyal, H. Eskandari, P. Abolmaesumi, S. Sojoudi, P. Gordon, L. Warren, R. N. Rohling, S. E. Salcudean, and M. Moradi, "Ultrasound RF time series for classification of breast lesions," IEEE Transactions on Medical Imaging, vol. 34, no. 2, pp. 652-661, 2015. DOI: 10.1109/TMI.2014.2365030.   DOI
3 N. Ke, C. Jeon-Hor, J. Y. Hon, C. Yong, N. Orhan, and S. Min-Ying, "Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI," Academic Radiology, vol. 15, no. 12, pp. 1513-1525, 2008. DOI: 10.1016/j.acra.2008.06.005.   DOI
4 W. Gomez, W. C. A. Pereira, and A. F. C. Infantosi, "Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound," IEEE Transactions on Medical Imaging, vo. 31, no. 10, pp. 1889-1899, 2012. DOI: 10.1109/TMI.2012.2206398.   DOI
5 N. Zulpe, and V. Pawar, "GLCM texture features for brain tumor classification," IJCSI International Journal of Computer Science Issues, vol. 9, no. 3, pp. 354-359. 2012.
6 H. Perter, M. Keven, and T. Abigail, "Diagnostic ultrasound; physics and equipment," Cambridge University Press, pp. 4-55, 2010.
7 A. Saleh, S. Mohamed, and J. Lu, "Analysis of GLCM parameters for texture classification on UMD database images," The Fifth International Conference on Advanced Communications and Computation (INFOCOMP), pp. 111-116, 2015. http://eprints.hud.ac.uk/id/eprint/25417/
8 Haralick, M. Robert, K. Shanmugam, and Its' H. Dinstein, "Textural features for image classification," Systems, Man and Cybernetics, IEEE Transactions. vol. 6, pp. 610-621, 1973.
9 National Health Insurance Statistical Yearbook, Health Insurance Review and Assessment Service, Korea, 2017.
10 https://www.probomedical.com/ge-logiq-s7-ultrasound-transducer-guide/
11 D. K. Shrikant, and M.P. Krushil, "Texture analysis of third ultrasound image for diagnosis of benign and malignant nodule using scaled conjugate gradient back propagation training neural network," International Journal of Computational Engineering & Management, vol. 16, pp. 33-38, 2013.
12 K. V. Mogatadakala, K. D. Donohue, C. W. Piccoli, and Forsberg, "Detection of breast lesion regions in ultrasound images using wavelets and order statistics," Medical Physic, vol. 33, no. 4, pp. 840-849, 2006. DOI: 10.1118/1.2174134.   DOI
13 Z. Zhou, S. Wu, K. J. Chang, W. R. Chen, Y. S. Chen, W. H. Kuo, C. C. Lin, and P. H. Tsui, "Classification of benign and malignant breast tumors in ultrasound images with posterior acoustic shadowing using half-contour features," Journal of Medical and Biological Engineering, vol. 35, pp. 178-187, 2015. DOI: 10.1007/s40846-015-0031-x.   DOI
14 T. Prabhakar, and S. Poonguzhali, "Analysis of contourlet texture feature extraction to classify the benign and malignant tumors from breast ultrasound images," International Journal of Engineering and Technology (IJET), vol. 6, no. 1, pp. 293-305, 2014.
15 M. M. Mehdy, P. Y. Ng, E. F. Shair, N. I. Saleh, and C. Gome, "Artificial neural networks in image processing for early detection of breast cancer," Computational and Mathematical Methods in Medicine, vol. 36, no. 3, pp. 124-150, 2017. DOI: 10.1155/2017/2610628.   DOI
16 D. S. Kim, N. Cho, D. Y. Kim, E. S. Ko, S. K. Yang, S. J. Kim, and W. K. Moon, "Mammographic and sonographic appearance of invasive micropapillary carcinoma of the breast," J. Korean Soc. Breast Screening, vol. 2, pp. 130-135, 2005.
17 R. Masud, M. Al-Rei, and C. Lokker, "Computer-aided detection for breast cancer screening in clinical settings," Scoping Review, JMIR Medical Informatics. vol. 7, no. 3, e12660, 2019. DOI: 10.2196/12660.   DOI
18 Minavathi, S. Murali, and M. S. Dinesh, "Classification of mass in breast ultrasound images using image processing techniques," International Journal of Computer Applications (0975-8887), vol. 42, no. 10, pp. 31-36, 2012.
19 M. I. Daoud, T. M. Bdair, M. Al-Najar, and R. Alazrai, "A fusionbased approach for breast ultrasound image classification using multiple-ROI texture and morphological analyses," Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, vol. 12, pp. 1-12, 2016. DOI: 10.1155/2016/6740956.   DOI
20 https://www.who.int/news-room/fact-sheets/detail/breast-cancer, 2021.
21 U. Ei, "Ultrasound screening of breast cancer," Breast Cancer, vol. 16, pp. 18-22, 2009. DOI: 10.1007/s12282-008-0082-8.   DOI