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For: Minhas Fu, Sabih D, Hussain M. Automated classification of liver disorders using ultrasound images. J Med Syst 2012;36:3163-72. [PMID: 22072280 DOI: 10.1007/s10916-011-9803-1] [Cited by in Crossref: 43] [Cited by in F6Publishing: 19] [Article Influence: 4.3] [Reference Citation Analysis]
Number Citing Articles
1 Subramanya MB, Kumar V, Mukherjee S, Saini M. A CAD system for B-mode fatty liver ultrasound images using texture features. Journal of Medical Engineering & Technology 2014;39:123-30. [DOI: 10.3109/03091902.2014.990160] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 1.3] [Reference Citation Analysis]
2 Jabarulla MY, Lee H. Computer aided diagnostic system for ultrasound liver images: A systematic review. Optik 2017;140:1114-26. [DOI: 10.1016/j.ijleo.2017.05.013] [Cited by in Crossref: 13] [Cited by in F6Publishing: 2] [Article Influence: 3.3] [Reference Citation Analysis]
3 Dey N, Bose S, Das A, Chaudhuri SS, Saba L, Shafique S, Nicolaides A, Suri JS. Effect of Watermarking on Diagnostic Preservation of Atherosclerotic Ultrasound Video in Stroke Telemedicine. J Med Syst 2016;40:91. [PMID: 26860914 DOI: 10.1007/s10916-016-0451-3] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 1.8] [Reference Citation Analysis]
4 Virmani J, Kumar V, Kalra N, Khandelwal N. A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound. J Med Eng Technol 2013;37:292-306. [PMID: 23701435 DOI: 10.3109/03091902.2013.794869] [Cited by in Crossref: 38] [Cited by in F6Publishing: 18] [Article Influence: 4.8] [Reference Citation Analysis]
5 Kim KB, Kim CW. Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography. Biomed Res Int 2015;2015:535894. [PMID: 26247023 DOI: 10.1155/2015/535894] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
6 Bharti P, Mittal D, Ananthasivan R. Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review. Ultrason Imaging 2017;39:33-61. [DOI: 10.1177/0161734616639875] [Cited by in Crossref: 18] [Cited by in F6Publishing: 11] [Article Influence: 3.6] [Reference Citation Analysis]
7 Owjimehr M, Danyali H, Helfroush MS, Shakibafard A. Staging of Fatty Liver Diseases Based on Hierarchical Classification and Feature Fusion for Back-Scan–Converted Ultrasound Images. Ultrason Imaging 2017;39:79-95. [DOI: 10.1177/0161734616649153] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 2.2] [Reference Citation Analysis]
8 Virmani J, Kumar V, Kalra N, Khandelwal N. Characterization of primary and secondary malignant liver lesions from B-mode ultrasound. J Digit Imaging 2013;26:1058-70. [PMID: 23412917 DOI: 10.1007/s10278-013-9578-7] [Cited by in Crossref: 42] [Cited by in F6Publishing: 24] [Article Influence: 6.0] [Reference Citation Analysis]
9 Sharma V, Juglan K. Automated Classification of Fatty and Normal Liver Ultrasound Images Based on Mutual Information Feature Selection. IRBM 2018;39:313-23. [DOI: 10.1016/j.irbm.2018.09.006] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 3.3] [Reference Citation Analysis]
10 Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JE, Jen Hong T, Sudarshan VK, Vijayananthan A, Yeong CH, Gudigar A, Ng KH. Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images. Comput Biol Med 2016;79:250-8. [PMID: 27825038 DOI: 10.1016/j.compbiomed.2016.10.022] [Cited by in Crossref: 60] [Cited by in F6Publishing: 38] [Article Influence: 12.0] [Reference Citation Analysis]
11 Nishida N, Yamakawa M, Shiina T, Kudo M. Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology. Hepatol Int 2019;13:416-21. [PMID: 30790230 DOI: 10.1007/s12072-019-09937-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 Goceri E, Shah ZK, Layman R, Jiang X, Gurcan MN. Quantification of liver fat: A comprehensive review. Comput Biol Med 2016;71:174-89. [PMID: 26945465 DOI: 10.1016/j.compbiomed.2016.02.013] [Cited by in Crossref: 37] [Cited by in F6Publishing: 26] [Article Influence: 7.4] [Reference Citation Analysis]
13 Virmani J, Kumar V, Kalra N, Khandelwal N. Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound. J Digit Imaging. 2014;27:520-537. [PMID: 24687642 DOI: 10.1007/s10278-014-9685-0] [Cited by in Crossref: 48] [Cited by in F6Publishing: 25] [Article Influence: 8.0] [Reference Citation Analysis]
14 Özyurt F, Tuncer T, Avci E, Koç M, Serhatlioğlu İ. A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network. Arab J Sci Eng 2019;44:3173-82. [DOI: 10.1007/s13369-018-3454-1] [Cited by in Crossref: 30] [Cited by in F6Publishing: 5] [Article Influence: 10.0] [Reference Citation Analysis]
15 Acharya UR, Fujita H, Sudarshan VK, Mookiah MRK, Koh JE, Tan JH, Hagiwara Y, Chua CK, Junnarkar SP, Vijayananthan A, Ng KH. An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images. Information Fusion 2016;31:43-53. [DOI: 10.1016/j.inffus.2015.12.007] [Cited by in Crossref: 31] [Cited by in F6Publishing: 12] [Article Influence: 6.2] [Reference Citation Analysis]
16 [DOI: 10.1117/12.2081949] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
17 Chen CI, Chen TB, Lu NH, Du WC, Liang CY, Liu KI, Hsu SY, Lin LW, Huang YH. Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study. Proc Inst Mech Eng H 2019;233:1100-12. [PMID: 31441386 DOI: 10.1177/0954411919871123] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 König T, Steffen J, Rak M, Neumann G, von Rohden L, Tönnies KD. Ultrasound texture-based CAD system for detecting neuromuscular diseases. Int J Comput Assist Radiol Surg 2015;10:1493-503. [PMID: 25451320 DOI: 10.1007/s11548-014-1133-6] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 2.3] [Reference Citation Analysis]
19 Subramanya MB, Kumar V, Mukherjee S, Saini M. SVM-Based CAC System for B-Mode Kidney Ultrasound Images. J Digit Imaging 2015;28:448-58. [PMID: 25537457 DOI: 10.1007/s10278-014-9754-4] [Cited by in Crossref: 22] [Cited by in F6Publishing: 10] [Article Influence: 4.4] [Reference Citation Analysis]