Scientific Journal

Herald of Advanced Information Technology

FORMING THE STACK OF TEXTURE FEATURES FOR LIVER ULTRASOUND IMAGES CLASSIFICATION
Abstract:
This article discusses the use of texture analysis methods to obtain informative features that describe the texture of liver ultrasound images. In total, 317 liver ultrasound images were analyzed, which were provided by the Institute of Nuclear Medicine and Radiation Diagnostics of NAMS of Ukraine. The images were taken by three different sensors (convex, linear, and linear sensor in increased signal level mode). Both images of patients with a normal liver condition and patients with specific liver disease (there were diseases such as: autoimmune hepatitis, Wilson's disease, hepatitis B and C, steatosis, and cirrhosis) were present in the database. Texture analysis was used for “Feature Construction”, which resulted in more than a hundred different informative features that made up a common stack. Among them, there are such features as: three authors’ patented features derived from the grey level co-occurrence matrix; features, obtained with the help of spatial sweep method (working by the principle of group method of data handling), which was applied to ultrasound images; statistical features, calculated on the images, brought to one scale with the help of differential horizontal and vertical matrices, which are proposed by the authors; greyscale pairs ensembles (found using the genetic algorithm), which identify liver pathology on images, transformed with the help of horizontal and vertical differentiations, in the best possible way. The resulting trait stack was used to solve the problem of binary classification (“norma-pathology”) of ultrasound liver images. A Machine Learning method, namely “Random Forest”, was used for this purpose. Before the classification, in order to obtain objective results, the total samples were divided into training (70 %), testing (20 %), and examining (10 %). The result was the best three Random Forest models separately for each sensor, which gave the following recognition rates: 93.4 % for the convex sensor, 92.9 % for the linear sensor, and 92 % for the reinforced linear sensor
Authors:
Keywords
DOI
10.15276/hait.04.2020.3
References
1. Croft, P., Altman, D. G., Deeks, J. J., Dunn, K. M., Hay, A. D., Hemingway, H., et al. “The science of clinical practice: Disease diagnosis or patient prognosis? Evidence about “what is likely to happen” should shape clinical practice”. BMC Med. 2015; 13(1): 8 p. DOI: 10.1186/s12916-014-0265-4. 
2. Boyd, A., Cain, O., Chauhan, A. & Webb, G. J. “Medical liver biopsy: indications, procedure and histopathology”. Frontline Gastroenterol. 2020; 11(1): 40–47. DOI: 10.1136/flgastro-2018-101139. 
3. Dumont, L., Larochelle-Brunet, F., Théoret, H., Riedl, R., Sénécal, S. & Léger, P. M. “Non-invasive brain stimulation in information systems research: A proof-of-concept study”. PLoS One. 2018; 13(7): 16 p. DOI: 10.1371/journal.pone.0201128. 
4. Mohammed, M. A., Al-Khateeb, B., Rashid, A. N., Ibrahim, D. A., Abd Ghani, M. K. & Mostafa, S. A. “Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images”. Comput Electr Eng. 2018; 70 p. DOI: 10.1016/j.compeleceng.2018.01.033. 
5. Mitrea, D., Nedevschi, S., Cenan, C., Lupsor, M. & Badea, R. “Exploring texture-based parameters, noninvasive characterization and modeling of diffuse liver diseases and liver cancer from ultrasound images”. WSEAS Trans Comput. 2007; 6(2): 283–290. 
6. Gao, S., Peng, Y., Guo, H., Liu, W., Gao, T., Xu, Y., et al. “Texture analysis and classification of ultrasound liver images”. In: Bio-Medical Materials and Engineering. 2014. p.1209–1216. DOI: 10.3233/BME-130922.
7. Ledley, R. S., Huang, H. K. & Rotolo, L. S. “A texture analysis method in classification of coal workers’ pneumoconiosis”. Comput Biol Med. 1975; 5(1): 53–67. DOI: 10.1016/0010-4825(75)90018-9. 
8. Thomas, R., Qin, L., Alessandrino, F., Sahu, S. P., Guerra, P. J., Krajewski, K. M., et al. “A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms”. Abdominal Radiology. 2019; Vol.44: 2501–2510. DOI: 10.1007/s00261-018-1832-5. 
9. Lubner, M. G., Smith, A. D., Sandrasegaran, K., Sahani, D. V. & Pickhardt, P. J. “CT texture analysis: Definitions, applications, biologic correlates, and challenges”. Radiographics. 2017; Vol.37: 1483– 1503. DOI: 10.1148/rg.2017170056. 
10. Lubner, M. G., Stabo, N., Abel, E. J., Munoz Del Rio, A. & Pickhardt, P. J. “CT textural analysis of large primary renal cell carcinomas: Pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes”. Am J Roentgenol. 2016; 207(1): 96–105. DOI: 10.2214/AJR.15.15451. 
11. Haider, M. A., Vosough, A., Khalvati, F., Kiss, A., Ganeshan, B. & Bjarnason, G. A. “CT texture analysis: A potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib”. Cancer Imaging. 2017; 17(1): 9 p. DOI: 10.1186/s40644-017-0106-8 
12. Sawyer, T. W., Chandra, S., Rice, P. F. S., Koevary, J. W. & Barton, J. K. “Three-dimensional texture analysis of optical coherence tomography images of ovarian tissue”. Phys Med Biol. 2018; 63(23): 29 p. DOI: 10.1088/1361-6560/aaefd2. 
13. Nastenko, Ie. & Yankovyi, І. “Klasifikator stanu pechinki u ditey z patologiyeyu gepatobiliarnoyi sistemi za teksturnimi statistikami ultrazvukovogo doslidjennia”. Biomedichna injeneriya i technologiya. 2019; 2: 15–23 (in Ukrainian). 
14. Alazawi, S. A., Shati, N. M. & Abbas, A. H. “Texture features extraction based on GLCM for face retrieval system”. Period Eng Nat Sci. 2019; 7(3): 1459–1467. DOI: 10.21533/pen.v7i3.787. 
15. Sharma, E. K., Priyanka, E., Kalsh, E. A. & Saini, E. K. “GLCM and its Features”. Int J Adv Res Electron Commun Eng. 2015; 4(8): 2180–2182. 
16. Xu, S. S. D., Chang, C. C., Su, C. T. & Phu, P. Q. “Classification of liver diseases based on ultrasound image texture features”. Appl Sci. 2019; 9(2): 25 р. DOI: 10.3390/app9020342. 
17. Raghesh Krishnan, K. & Radhakrishnan, S. “Focal and diffused liver disease classification from ultrasound images based on isocontour segmentation”. IET Image Process. 2015; 9(4): 261–270. DOI: 10.1049/iet-ipr.2014.0202. 
18. Yameng, C., Gengxin, S., Yiming, L. & Jinpeng, Z. “An effective method for cirrhosis recognition based on multi-feature fusion”. 2018. 227 p. DOI: 10.1117/12.2304733. 
19. Kruglyi, V. & Nastenko, Ie. “Formirovanie informativnih priznakov dlia zadachi klassifikaciyi patologiya/norma po izobrajeniyu UZI pecheni pacienta”. Scientific Discussion. 2019;1(31):57–59 (in Russian). 
20. Nastenko, Ie., Dykan, I., Tarasiuk, B., Pavlov, V., Nosovets, O., Babenko, V., Kruglyi, V., Dyba, M. & Soloduschenko, V. “Klassifikaciya staniv pechinki pri difuznih zahvoruvanniah na osnovi statistichnih pokaznikiv teksturi ultrazvukovih zobrazhen’ ta MGUA”. Induktivne modelliuvannia skladnih sistem. 2019; 11: 54–66 (in Ukrainian). 
21. Teng, G., Xiao, J., He, Y., Zheng, T. & He, C. “Use of group method of data handling for transport energy demand modeling”. Energy Sci Eng. 2017; 5(5): 302–317. DOI: 10.1002/ese3.176. 
22. Nastenko, Ie., Konoval, O., Nosovets, O. & Pavlov, V. “Set Classification”. In: Techno-Social Systems for Modern Economical and Governmental Infrastructures. 2018. p.44–83. DOI: 10.4018/978-1- 5225-5586-5.ch003. 
23. Hrishko, D., Trofimenko, O. & Pavlov, V. “Strukturniy sintez za kriteriyem tochnosti v zadachi klasifikaciyi obyektiv mnojin”. Scientific Discussion. 2019; 1(31): 50–52 (in Ukrainian). 
24. Nystrup, P., Lindström, E., Pinson, P. & Madsen, H. “Temporal hierarchies with autocorrelation for load forecasting”. Eur J Oper Res. 2020; 280(3): 876–88. DOI: 10.1016/j.ejor.2019.07.061. 
25. Scalco, E. & Rizzo, G. “Texture analysis of medical images for radiotherapy applications”. British Journal of Radiology. 2017; Vol. 90: 15p. DOI: 10.1259/bjr.20160642. 
26. HimaBindu, G., Anuradha, C. & Chandra Murty, P. S. R. “Assessment of combined shape, color and textural features for video duplication”. Trait du Signal. 2019; 36(2): 193–199. DOI: 10.18280/ts.360210. 
27. Hall, M. A. “Correlation-Based Feature Selection for Machine Learning”. 1999. 109 p. 
28. Astivia, O. L. O. & Zumbo, B. D. “Population models and simulation methods: The case of the Spearman rank correlation”. Br J Math Stat Psychol. 2017; 70(3): 347–367. DOI: 10.1111/bmsp.12085. 
29. Ghaheri, A., Shoar, S., Naderan, M. & Hoseini, S. S. “The Applications of Genetic Algorithms in Medicine”. Oman medical journal. 2015; Vol.30 No 6: 406–416. DOI: 10.5001/omj.2015.82.
30. Belgiu, M. & Drăguţ, L. “Random forest in remote sensing: A review of applications and future directions”, ISPRS Journal of Photogrammetry and Remote Sensing. 2016; Vol.114: 24-31. DOI: 10.1016/j.isprsjprs.2016.01.011.
Published:
Last download:
18 Oct 2021

Contents


[ © KarelWintersky ] [ All articles ] [ All authors ]
[ © Odessa National Polytechnic University, 2018.]