A complementary patch-based histogram analysis for quantifying muscle tissue in ultrasound imaging
You have full access to this open access article
Downloads
Abstract
Introduction: Ultrasound imaging is widely used for muscle assessment due to its non-invasive nature and real-time imaging capabilities. Histogram-based echotexture analysis has proven to be a valuable tool for quantifying muscle tissue composition using features such as echointensity (EI) and echovariation (EV), especially in neuromuscular and neurological disorders. However, variability in region of interest (ROI) selection and image processing methods can significantly affect the extracted echotexture features. This study presents a novel histogram-based analysis approach to investigate the effects of subdividing a single ROI into different patch sizes for muscle tissue assessment. By focusing on the EI and EV of the gastrocnemius medialis in a stroke patient, this research aims to refine quantitative ultrasound analysis to improve clinical applicability.
Case presentation: One stroke patient was randomly selected from a previously collected dataset to perform a complementary analysis. The initially selected grey-scale ROI was extracted and divided into patches of different sizes (10×10, 20×20, 30×30, 40×40 and 50×50 pixels). The EI and EV of each patch were calculated, and their distributions were analyzed using descriptive statistics and correlation methods.
Results: The EV values for patches of sizes 10×10, 20×20, 30×30, 40×40, and 50×50 were 26.48, 24.58, 20.44, 16.78, and 10.38, respectively, which deviated significantly from the original ROI value of 45.54. In contrast, the EI values remained around 81 across all patch sizes, indicating that varying patch sizes did not affect EI.
Conclusions: Patch-based histogram analysis offers a complementary method for assessing muscle texture in ultrasound. While EI appears to be robust to ROI subdivision, EV shows variability, raising concerns about its reliability when small patches are used. Standardized methods and future research with larger datasets are needed to optimize echotexture analysis and ensure reproducibility in clinical practice.
Keywords
- Ultrasonography
- Muscle tissue
- Echotexture analysis
- Histogram
- Stroke
References
Whittaker JL, Stokes M. Ultrasound imaging and muscle function. J Orthop Sports Phys Ther. 2011;41(8):572–80. doi:10.2519/jospt.2011.3682 DOI: https://doi.org/10.2519/jospt.2011.3682
Chang KV, Wu WT, Özçakar L. Ultrasound imaging and rehabilitation of muscle disorders: part 1. Traumatic injuries. Am J Phys Med Rehabil. 2019;98(12):1133–41. doi:10.1097/PHM.0000000000001307 DOI: https://doi.org/10.1097/PHM.0000000000001307
Hodges PW. Ultrasound imaging in rehabilitation: just a fad? J Orthop Sports Phys Ther. 2005;35(6):333–7. doi:10.2519/jospt.2005.0106 DOI: https://doi.org/10.2519/jospt.2005.0106
Stokes M, Hides J, Nassiri DK. Musculoskeletal ultrasound imaging: diagnostic and treatment aid in rehabilitation. Phys Ther Rev. 1997;2(2):73–92. doi: 10.1179/ptr.1997.2.2.73 DOI: https://doi.org/10.1179/ptr.1997.2.2.73
Naruse M, Trappe S, Trappe TA. Human skeletal muscle size with ultrasound imaging: a comprehensive review. J Appl Physiol (1985). 2022;132(5):1267–79. doi:10.1152/japplphysiol.00041.2022 DOI: https://doi.org/10.1152/japplphysiol.00041.2022
Materka A, Strzelecki M. Texture Analysis Methods - A Review. Technical University of Lodz, Institute of Electronics, COST B11 Report. 1998.
Paris MT, Mourtzakis M. Muscle composition analysis of ultrasound images: a narrative review of texture analysis. Ultrasound Med Biol. 2021;47(4):880–95. doi:10.1016/j.ultrasmedbio.2020.12.012 DOI: https://doi.org/10.1016/j.ultrasmedbio.2020.12.012
Nailon WH. Texture analysis methods for medical image characterisation. Biomed Imaging. 2010;75:100. doi:10.5772/8912 DOI: https://doi.org/10.5772/8912
Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37(5):1483–503. doi:10.1148/rg.2017170056 DOI: https://doi.org/10.1148/rg.2017170056
Mailloux GE, Bertrand M, Stampfler R, Ethier S. Local histogram information content of ultrasound B-mode echographic texture. Ultrasound Med Biol. 1985;11(5):743–50. doi:10.1016/0301-5629(85)90108-5 DOI: https://doi.org/10.1016/0301-5629(85)90108-5
Pillen S, Van Alfen N. Muscle ultrasound from diagnostic tool to outcome measure—Quantification is the challenge. Muscle Nerve. 2015;52(3):319–20. doi:10.1002/mus.24613 DOI: https://doi.org/10.1002/mus.24613
Pillen S, Arts IM, Zwarts MJ. Muscle ultrasound in neuromuscular disorders. Muscle Nerve. 2008;37(6):679–93. doi:10.1002/mus.21015 DOI: https://doi.org/10.1002/mus.21015
Shannon CE. A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev. 2001;5(1):3–55. doi:10.1145/584091.584093 DOI: https://doi.org/10.1145/584091.584093
Sogawa K, Nodera H, Takamatsu N, Mori A, Yamazaki H, Shimatani Y, et al. Neurogenic and myogenic diseases: quantitative texture analysis of muscle US data for differentiation. Radiology. 2017;283(2):492–8. doi:10.1148/radiol.2016160826 DOI: https://doi.org/10.1148/radiol.2016160826
Ismail C, Zabal J, Hernandez HJ, Woletz P, Manning H, Teixeira C, et al. Diagnostic ultrasound estimates of muscle mass and muscle quality discriminate between women with and without sarcopenia. Front Physiol. 2015;6:302. doi:10.3389/fphys.2015.00302 DOI: https://doi.org/10.3389/fphys.2015.00302
Pagán-Conesa A, García-Ortiz MT, Salmerón-Martínez EJ, Moya-Martínez A, López-Prats F. Diagnostic ultrasound shows reversal of supraspinatus muscle atrophy following arthroscopic rotator cuff repair. Arthroscopy. 2021;37(10):3039–48. doi:10.1016/j.arthro.2021.04.039 DOI: https://doi.org/10.1016/j.arthro.2021.04.039
Harris-Love MO, Gonzales TI, Wei Q, Ismail C, Zabal J, Woletz P, et al. Association between muscle strength and modeling estimates of muscle tissue heterogeneity in young and old adults. J Ultrasound Med. 2019;38(7):1757–68. doi:10.1002/jum.14864 DOI: https://doi.org/10.1002/jum.14864
Calvo-Lobo C, Useros-Olmo AI, Almazán-Polo J, Martín-Sevilla M, Romero-Morales C, Sanz-Corbalán I, et al. Quantitative ultrasound imaging pixel analysis of the intrinsic plantar muscle tissue between hemiparesis and contralateral feet in post-stroke patients. Int J Environ Res Public Health. 2018;15(11):2519. doi:10.3390/ijerph15112519 DOI: https://doi.org/10.3390/ijerph15112519
Martínez-Payá JJ, Ríos-Díaz J, Del Baño-Aledo ME, Tembl-Ferrairó JI, Vazquez-Costa JF, Medina-Mirapeix F. Quantitative muscle ultrasonography using textural analysis in amyotrophic lateral sclerosis. Ultrason Imaging. 2017;39(6):357–68. doi:10.1177/0161734617711370 DOI: https://doi.org/10.1177/0161734617711370
Asadi B, Pujol-Fuentes C, Carcasona-Otal A, Calvo S, Herrero P, Lapuente-Hernández D. Characterizing muscle tissue quality post-stroke: echovariation as a clinical indicator. J Clin Med. 2024;13(24). doi:10.3390/jcm13247800 DOI: https://doi.org/10.3390/jcm13247800
Akazawa N, Harada K, Okawa N, Kishi M, Tamura K, Moriyama H. Changes in quadriceps thickness and echo intensity in chronic stroke survivors: a 3-year longitudinal study. J Stroke Cerebrovasc Dis. 2021;30(3):105543. doi: 10.1016/j.jstrokecerebrovasdis.2020.105543 DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105543
Heckmatt JZ, Leeman S, Dubowitz V. Ultrasound imaging in the diagnosis of muscle disease. J Pediatr. 1982;101(5):656–60. doi:10.1016/s0022-3476(82)80286-2 DOI: https://doi.org/10.1016/S0022-3476(82)80286-2
Molinari F, Caresio C, Acharya UR, Mookiah MR, Minetto MA. Advances in quantitative muscle ultrasonography using texture analysis of ultrasound images. Ultrasound Med Biol. 2015;41(9):2520–32. doi:10.1016/j.ultrasmedbio.2015.04.021 DOI: https://doi.org/10.1016/j.ultrasmedbio.2015.04.021
Sahinis C, Kellis E. Hamstring muscle quality properties using texture analysis of ultrasound images. Ultrasound Med Biol. 2023;49(2):431–40. doi:10.1016/j.ultrasmedbio.2022.09.011 DOI: https://doi.org/10.1016/j.ultrasmedbio.2022.09.011
Escriche-Escuder A, Trinidad-Fernández M, Pajares B, Iglesias-Campos M, Alba E, García-Almeida JM, et al. Responsiveness of the new index muscular echotexture in women with metastatic breast cancer: an exercise intervention study. Sci Rep. 2022;12(1):15148. doi:10.1038/s41598-022-19532-7 DOI: https://doi.org/10.1038/s41598-022-19532-7
Del-Canto-Fernández A, Calleja-Martínez P, Descalzo-Hoyas B, Rodríguez-Posada S, Cuenca-Zaldívar N, Fernández-Carnero S, et al. The application of image texture analysis techniques on the effects of dry needling versus placebo in low-back pain patients: a pilot study. Appl Sci. 2022;12(11):5556. doi:10.3390/app12115556 DOI: https://doi.org/10.3390/app12115556
Shrestha B, Dunn L. The declaration of Helsinki on medical research involving human subjects: a review of seventh revision. J Nepal Health Res Counc. 2020;17(4):548–52. doi:10.33314/jnhrc.v17i4.1042 DOI: https://doi.org/10.33314/jnhrc.v17i4.1042
Caresio C, Molinari F, Emanuel G, Minetto MA. Muscle echo intensity: reliability and conditioning factors. Clin Physiol Funct Imaging. 2015;35(5):393–403. doi:10.1111/cpf.12175 DOI: https://doi.org/10.1111/cpf.12175
Bujang MA. An elaboration on sample size determination for correlations based on effect sizes and confidence interval width: a guide for researchers. Restor Dent Endod. 2024;49(2):e21. doi:10.5395/rde.2024.49.e21 DOI: https://doi.org/10.5395/rde.2024.49.e21
Hunsberger S, Long L, Reese SE, Hong GH, Myles IA, Zerbe CS, et al. Rank correlation inferences for clustered data with small sample size. Stat Neerl. 2022;76(3):309–30. doi:10.1111/stan.12261 DOI: https://doi.org/10.1111/stan.12261
Kermani A, Ayatollahi A, Talebi M. Segmentation of medical ultrasound image based on local histogram range image. In: 3rd International Conference on Biomedical Engineering and Informatics; 2010. IEEE. doi:10.1109/BMEI.2010.5639996 DOI: https://doi.org/10.1109/BMEI.2010.5639996
Zhu Q. Ultrasonic image analysis of cardiac space-occupying diseases based on grey clustering algorithm. In: 2023 International Conference on Telecommunications, Electronics and Informatics (ICTEI); 2023. IEEE. doi:10.1109/ICTEI60496.2023.00173 DOI: https://doi.org/10.1109/ICTEI60496.2023.00173
Zhang D, Liu Y, Yang Y, Xu M, Yan Y, Qin Q. A region-based segmentation method for ultrasound images in HIFU therapy. Med Phys. 2016;43(6):2975–89. doi:10.1118/1.4950706 DOI: https://doi.org/10.1118/1.4950706
Dos Santos E, Yoshizawa M, Tanaka A, Saijo Y, Iwamoto T. Detection of luminal contour using fuzzy clustering and mathematical morphology in intravascular ultrasound images. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:3471–4. doi:10.1109/iembs.2005.1617226 DOI: https://doi.org/10.1109/IEMBS.2005.1617226
Downloads
License
Copyright (c) 2025 Borhan Asadi, Sarkout Abdi, Luis Pérez-Espallargas, Noureddin Nakhostin Ansari, Diego Lapuente-Hernández
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.