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For: Brynolfsson P, Nilsson D, Torheim T, Asklund T, Karlsson CT, Trygg J, Nyholm T, Garpebring A. Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters. Sci Rep 2017;7:4041. [PMID: 28642480 DOI: 10.1038/s41598-017-04151-4] [Cited by in Crossref: 47] [Cited by in F6Publishing: 32] [Article Influence: 9.4] [Reference Citation Analysis]
Number Citing Articles
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9 Li W, Jiang Z, Guan Y, Chen Y, Huang X, Liu S, He J, Zhou Z, Ge Y. Whole-lesion Apparent Diffusion Coefficient First- and Second-Order Texture Features for the Characterization of Rectal Cancer Pathological Factors. J Comput Assist Tomogr. 2018;42:642-647. [PMID: 29613992 DOI: 10.1097/rct.0000000000000731] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
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11 Lee SE, Jung JY, Nam Y, Lee SY, Park H, Shin SH, Chung YG, Jung CK. Radiomics of diffusion-weighted MRI compared to conventional measurement of apparent diffusion-coefficient for differentiation between benign and malignant soft tissue tumors. Sci Rep 2021;11:15276. [PMID: 34315971 DOI: 10.1038/s41598-021-94826-w] [Reference Citation Analysis]
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13 Löfstedt T, Brynolfsson P, Asklund T, Nyholm T, Garpebring A. Gray-level invariant Haralick texture features. PLoS One 2019;14:e0212110. [PMID: 30794577 DOI: 10.1371/journal.pone.0212110] [Cited by in Crossref: 35] [Cited by in F6Publishing: 16] [Article Influence: 11.7] [Reference Citation Analysis]
14 Tang TT, Zawaski JA, Francis KN, Qutub AA, Gaber MW. Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study. Sci Rep 2019;9:12529. [PMID: 31467303 DOI: 10.1038/s41598-019-48738-5] [Cited by in Crossref: 14] [Cited by in F6Publishing: 7] [Article Influence: 4.7] [Reference Citation Analysis]
15 Do QN, Lewis MA, Xi Y, Madhuranthakam AJ, Happe SK, Dashe JS, Lenkinski RE, Khan A, Twickler DM. MRI of the Placenta Accreta Spectrum (PAS) Disorder: Radiomics Analysis Correlates With Surgical and Pathological Outcome. J Magn Reson Imaging 2019;51:936-46. [DOI: 10.1002/jmri.26883] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
16 Azhar M, Schenone S, Anderson A, Gee T, Cooper J, van der Mark W, Hillman JR, Yang K, Thrush SF, Delmas P. A framework for multiscale intertidal sandflat mapping: A case study in the Whangateau estuary. ISPRS Journal of Photogrammetry and Remote Sensing 2020;169:242-52. [DOI: 10.1016/j.isprsjprs.2020.09.013] [Reference Citation Analysis]
17 Garpebring A, Brynolfsson P, Kuess P, Georg D, Helbich TH, Nyholm T, Löfstedt T. Density estimation of grey-level co-occurrence matrices for image texture analysis. Phys Med Biol 2018;63:195017. [PMID: 30088815 DOI: 10.1088/1361-6560/aad8ec] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Pain C, Kriechbaumer V, Kittelmann M, Hawes C, Fricker M. Quantitative analysis of plant ER architecture and dynamics. Nat Commun 2019;10:984. [PMID: 30816109 DOI: 10.1038/s41467-019-08893-9] [Cited by in Crossref: 23] [Cited by in F6Publishing: 17] [Article Influence: 7.7] [Reference Citation Analysis]
19 Burian E, Subburaj K, Mookiah MRK, Rohrmeier A, Hedderich DM, Dieckmeyer M, Diefenbach MN, Ruschke S, Rummeny EJ, Zimmer C, Kirschke JS, Karampinos DC, Baum T. Texture analysis of vertebral bone marrow using chemical shift encoding-based water-fat MRI: a feasibility study. Osteoporos Int 2019;30:1265-74. [PMID: 30903208 DOI: 10.1007/s00198-019-04924-9] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
20 Zhao Y, Lu Y, Li X, Zheng Y, Yin B. The Evaluation of Radiomic Models in Distinguishing Pilocytic Astrocytoma From Cystic Oligodendroglioma With Multiparametric MRI. J Comput Assist Tomogr 2020;44:969-76. [PMID: 32976261 DOI: 10.1097/RCT.0000000000001088] [Reference Citation Analysis]
21 Shur J, Blackledge M, D'Arcy J, Collins DJ, Bali M, O'Leach M, Koh DM. MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study. Eur Radiol Exp 2021;5:2. [PMID: 33462642 DOI: 10.1186/s41747-020-00199-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
22 Bah M, Hafiane A, Canals R. Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images. Remote Sensing 2018;10:1690. [DOI: 10.3390/rs10111690] [Cited by in Crossref: 59] [Cited by in F6Publishing: 6] [Article Influence: 14.8] [Reference Citation Analysis]
23 Simpson G, Ford JC, Llorente R, Portelance L, Yang F, Mellon EA, Dogan N. Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features. Phys Med 2020;80:209-20. [PMID: 33190077 DOI: 10.1016/j.ejmp.2020.10.029] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
24 Blackburn J, Alves MJ, Aslan MT, Cevik L, Zhao J, Czeisler CM, Otero JJ. Astrocyte regional heterogeneity revealed through machine learning-based glial neuroanatomical assays. J Comp Neurol 2021;529:2464-83. [PMID: 33410136 DOI: 10.1002/cne.25105] [Reference Citation Analysis]
25 Brabec J, Lennartsson F. Editorial for "Investigation of the Inter- and Intra-Scanner Reproducibility and Repeatability of Radiomics Features in Magnetic Resonance Imaging". J Magn Reson Imaging 2022. [PMID: 35403768 DOI: 10.1002/jmri.28190] [Reference Citation Analysis]
26 Reynaert N. PET and MRI based RT treatment planning: Handling uncertainties. Cancer Radiother 2019;23:753-60. [PMID: 31427076 DOI: 10.1016/j.canrad.2019.08.002] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
27 Rai R, Holloway LC, Brink C, Field M, Christiansen RL, Sun Y, Barton MB, Liney GP. Multicenter evaluation of MRI-based radiomic features: A phantom study. Med Phys 2020;47:3054-63. [PMID: 32277703 DOI: 10.1002/mp.14173] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
28 Bialek EJ, Malkowski B. Is the level of diffusion restriction in celiac and cervico-thoracic sympathetic ganglia helpful in their proper recognition on PSMA ligand PET/MR? Nuklearmedizin 2020;59:300-7. [PMID: 32005043 DOI: 10.1055/a-1079-3855] [Reference Citation Analysis]
29 Daniel M, Kuess P, Andrzejewski P, Nyholm T, Helbich T, Polanec S, Dragschitz F, Goldner G, Georg D, Baltzer P. Impact of androgen deprivation therapy on apparent diffusion coefficient and T2w MRI for histogram and texture analysis with respect to focal radiotherapy of prostate cancer. Strahlenther Onkol 2019;195:402-11. [PMID: 30478670 DOI: 10.1007/s00066-018-1402-3] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
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31 Mitchell-Hay RN, Ahearn TS, Murray AD, Waiter GD. Investigation of the Inter- and Intrascanner Reproducibility and Repeatability of Radiomics Features in T1-Weighted Brain MRI. J Magn Reson Imaging 2022. [PMID: 35396777 DOI: 10.1002/jmri.28191] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Strzelecki M, Kociołek M, Materka A. On the Influence of Image Features Wordlength Reduction on Texture Classification. In: Pietka E, Badura P, Kawa J, Wieclawek W, editors. Information Technology in Biomedicine. Cham: Springer International Publishing; 2019. pp. 15-26. [DOI: 10.1007/978-3-319-91211-0_2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
33 Madusanka N, Choi HK, So JH, Choi BK. Alzheimer's Disease Classification Based on Multi-feature Fusion. Curr Med Imaging Rev 2019;15:161-9. [PMID: 31975662 DOI: 10.2174/1573405614666181012102626] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
34 Thomas R, Qin L, Alessandrino F, Sahu SP, Guerra PJ, Krajewski KM, Shinagare A. A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms. Abdom Radiol (NY) 2019;44:2501-10. [PMID: 30448920 DOI: 10.1007/s00261-018-1832-5] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
35 Gola J, Webel J, Britz D, Guitar A, Staudt T, Winter M, Mücklich F. Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels. Computational Materials Science 2019;160:186-96. [DOI: 10.1016/j.commatsci.2019.01.006] [Cited by in Crossref: 40] [Cited by in F6Publishing: 3] [Article Influence: 13.3] [Reference Citation Analysis]
36 Gillingham N, Chandarana H, Kamath A, Shaish H, Hindman N. Bosniak IIF and III Renal Cysts: Can Apparent Diffusion Coefficient–Derived Texture Features Discriminate Between Malignant and Benign IIF and III Cysts? Journal of Computer Assisted Tomography 2019;43:485-92. [DOI: 10.1097/rct.0000000000000851] [Cited by in Crossref: 2] [Article Influence: 0.7] [Reference Citation Analysis]
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38 Khan FA, Voß U, Pound MP, French AP. Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning. Front Plant Sci 2020;11:1275. [PMID: 32983190 DOI: 10.3389/fpls.2020.01275] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
39 Buch K, Kuno H, Qureshi MM, Li B, Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. J Appl Clin Med Phys 2018;19:253-64. [PMID: 30369010 DOI: 10.1002/acm2.12482] [Cited by in Crossref: 27] [Cited by in F6Publishing: 23] [Article Influence: 6.8] [Reference Citation Analysis]
40 Chirra P, Leo P, Yim M, Bloch BN, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE. Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. J Med Imaging (Bellingham) 2019;6:024502. [PMID: 31259199 DOI: 10.1117/1.JMI.6.2.024502] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
41 Liu F, Lin G, Tseng J, Chao A, Huang H, Chou H, Chang Y, Yen T, Lai C. Measuring Heterogeneity in 18F-Fluorodeoxyglucose Positron Emission Tomography Images for Classifying Metastatic and Benign Bone Lesions in Patients with Cervical Cancer. J Med Biol Eng 2021;41:924-33. [DOI: 10.1007/s40846-021-00671-7] [Reference Citation Analysis]
42 Varghese BA, Cen SY, Hwang DH, Duddalwar VA. Texture Analysis of Imaging: What Radiologists Need to Know. AJR Am J Roentgenol. 2019;212:520-528. [PMID: 30645163 DOI: 10.2214/ajr.18.20624] [Cited by in Crossref: 61] [Cited by in F6Publishing: 30] [Article Influence: 20.3] [Reference Citation Analysis]
43 Hapsari RK, Miswanto M, Rulaningtyas R, Suprajitno H, Seng GH, Bruno A. Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image. International Journal of Biomedical Imaging 2022;2022:1-11. [DOI: 10.1155/2022/5336373] [Reference Citation Analysis]