Browse > Article

Analysis of Malignant Tumor Using Texture Characteristics in Breast Ultrasonography  

Cho, Jin-Young (Wellness hospital of radiology)
Ye, Soo-Young (Dept. of Radiological Science, College of Health Science, Catholic University of Pusan)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.20, no.2, 2019 , pp. 70-77 More about this Journal
Abstract
Breast ultrasound readings are very important to diagnose early breast cancer. In Ultrasonic inspection, it shows a significant difference in image quality depending on the ultrasonic equipment, and there is a large difference in diagnosis depending on the experience and skill of the inspector. Therefore, objective criteria are needed for accurate diagnosis and treatment. In this study, we analyzed texture characteristics by applying GLCM (Gray Level Co-occurrence Matrix) algorithm and extracted characteristic parameters and diagnosed breast cancer using neural network classifier. Breast ultrasound images were classified into normal, benign and malignant tumors and six texture parameters were extracted. Fourteen cases of normal, malignant and benign tumor diagnosed by mammography were studied by using the extracted six parameters and learning by multi - layer perceptron neural network back propagation learning method. As a result of classification using 51 normal images, 62 benign tumor images, and 74 malignant tumor images of the learned model, the classification rate was 95.2%.
Keywords
Breast cancer; Ultrasonography; Gray Level Co-occurrence Matrix(GLCM); Neural network classification;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 S. M. Kim, S. Y. Ye, "Analysis on the Occurrence Factors of High-Risk Diseases of Pregnant Women by the Degree of Obesity," The Korea Institute of Convergence Signal Processing, pp. 118-124, 2018.
2 B. E. Adrada, S. Krishnamurthy, S. Carkaci, F. E P. Monetto, "Unusual Benign Tumors of the Breast," Journal of Clinical Imagung Science, vol. 5, no.2,pp. 1-5, 2015.   DOI
3 Doi, Kunio., "Current status and future potential of computer-aided diagnosis in medical imaging," The British Journal of Radiology, pp. S3-S19, 2005.
4 G.Y. Yoon, J. H. Cha, H. H. Kim, H. J. S., E. Y. C., and W. J. Choi, "Sonographic features that can be used to differentiate between small triple-negative breast cancer and fibroadenoma," Ultrasonography, vol. 37, no. 2, pp. 149-156, 2018.   DOI
5 J. Levman., E. Warner, P. Causer, A. Martel "Semi-Automatic Region-of-Interest Segmentation Based Computer-Aided Diagnosis of Mass Lesions from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Breast Cancer Screening," J Digit Imaging, vol. 27, no. 5, pp. 670-678, 2014.   DOI
6 J. Virmani, V. Kumar, N. Kalra, N. Khandelwal, "Characterization of Primary and Secondary Malignant Liver Lesions from B-Mode Ultrasound," J Digit Imaging., vol. 26, no. 6, pp. 1058-1070, 2013.   DOI
7 R. Guo, G. Lu, B. Fei,"Ultrosound sound imaging Technologies for Breast Cancer detection and management -A review," Comput. Math Methods Med. vol.44, no. 1, pp. 37-70, 2018.
8 W. K. Moon, C.M. Lo, J. M. Chang, C.S. Huang, J.H. Chen, R.F. Chang, "Quantitative Ultrasound Analysis for Classification of BI-RADS Category 3 Breast Masses," J Digit Imaging., vol.26, no.6, pp.1091-1098, 2013.   DOI
9 S. I. Jung, "Ultrasonography of ovarian masses using a pattern recognition approach," Ultrasonography, vol. 34, no. 3, pp. 173-182, 2015.   DOI
10 Z. Zhou, S. Wu, K.J. Chang, W.R. Chen, Y.S. Chen, W.H. Kuo, C.-C. Lin, P.H. Tsui, "Classification of Benign and Malignant Beast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features," J. Med. Biol. Eng., vol. 35, pp. 178-187, 2015.   DOI
11 S. M. Kim, H. H., J. M. Park, Y. J. Choi, H. S. Yoon, J. H. Sohn, M. H. Baek, Y. N. Kim, Y. M. Chae, J. J June, J.w. Lee, Y. H. Jeon, "A Comparison of Logistic Regression Analysis and an Artificial Neural Network Using the BI-RADS Lexicon for Ultrasonography in Conjunction with Introbserver Variability," J Digit Imaging, vol. 25, no. 5, pp. 599-606, 2012.   DOI
12 M. M. Mehdy, P.Y.Ng, E. F. Shair, N. I. Md 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.