FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / BMC Vet Res / Selection of density standard and X-ray tube settings for co...
BMC veterinary research2025; 21(1); 165; doi: 10.1186/s12917-025-04591-5

Selection of density standard and X-ray tube settings for computed digital absorptiometry in horses using the k-means clustering algorithm.

Abstract: In veterinary medicine, conventional radiography is the first-choice method for most diagnostic imaging applications in both small animal and equine practice. One direction in its development is the integration of bone density evaluation and artificial intelligence-assisted clinical decision-making, which is expected to enhance and streamline veterinarians' daily practices. One such decision-support method is k-means clustering, a machine learning and data mining technique that can be used clinically to classify radiographic signs into healthy or affected clusters. The study aims to investigate whether the k-means clustering algorithm can differentiate cortical and trabecular bone in both healthy and affected horse limbs. Therefore, identifying the optimal computed digital absorptiometry parameters was necessary. Results: Five metal-made density standards, made of pure aluminum, aluminum alloy (duralumin), cuprum alloy, iron-nickel alloy, and iron-silicon alloy, and ten X-ray tube settings were evaluated for the radiographic imaging of equine distal limbs, including six healthy limbs and six with radiographic signs of osteoarthritis. Density standards were imaged using ten combinations of X-ray tube settings, ranging from 50 to 90 kV and 1.2 to 4.0 mAs. The relative density in Hounsfield units was firstly returned for both bone types and density standards, then compared, and finally used for clustering. In both healthy and osteoarthritis-affected limbs, the relative density of the long pastern bone (the proximal phalanx) differed between bone types, allowing the k-means clustering algorithm to successful differentiate cortical and trabecular bone. Conclusions: Density standard made of duralumin, along with the 60 kV, 4.0 mAs X-ray tube settings, yielded the highest clustering metric values and was therefore considered optimal for further research. We believe that the identified optimal computed digital absorptiometry parameters may be recommended for further researches on the relative quantification of conventional radiographs and for distal limb examination in equine veterinary practice.
© 2025. The Author(s).
Get Full Text
Publication Date: 2025-03-13 PubMed ID: 40082938PubMed Central: PMC11905476DOI: 10.1186/s12917-025-04591-5Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research study investigates the use of the k-means clustering algorithm, a machine learning technique, to differentiate between different types of bone in both healthy and disease-affected horse limbs, using computed digital absorptiometry. The study found that a density standard made from duralumin, alongside specific X-ray tube settings, was the most effective for this purpose.

Research Purpose and Methodology

  • The study had two main objectives. The first was to determine if the k-means clustering algorithm could differentiate between cortical and trabecular bone types in the limbs of healthy and disease-affected horses. The second objective was to identify the best computed digital absorptiometry parameters for achieving this.
  • The researchers used five metal-made density standards and ten X-ray tube settings to image the distal limbs of six healthy horses and six horses with signs of osteoarthritis.
  • The ability of the k-means clustering algorithm to differentiate between the bone types was then assessed using the relative density Hounsfield units of both bone types and the density standards.

Results

  • Both sets of horse limbs showed different relative densities in the long pastern bone, which allowed the k-means clustering algorithm to distinguish between cortical and trabecular bone types.
  • Optimal imaging was achieved with a duralumin density standard and X-ray tube settings of 60 kV and 4.0 mAs. This combination resulted in the highest clustering metric values.

Implications and Conclusions

  • The findings suggest that the identified computed digital absorptiometry parameters could be used in future research into the relative quantification of conventional radiographs.
  • This could also prove useful in equine veterinary practice for examinations of distal limb health, as it enables the differentiation of healthy and disease-affected structures.
  • As such, the incorporation of the k-means clustering algorithm and optimal imaging settings could well enhance and streamline daily practice for veterinarians treating horses.

Cite This Article

APA
Turek B, Pawlikowski M, Jankowski K, Borowska M, Skierbiszewska K, Jasiński T, Domino M. (2025). Selection of density standard and X-ray tube settings for computed digital absorptiometry in horses using the k-means clustering algorithm. BMC Vet Res, 21(1), 165. https://doi.org/10.1186/s12917-025-04591-5

Publication

BMC veterinary research
ISSN: 1746-6148
NlmUniqueID: 101249759
Country: England
Language: English
Volume: 21
Issue: 1
Pages: 165
PII: 165

Researcher Affiliations

Turek, Bernard
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS - SGGW), Nowoursynowska 100, Warsaw, 02-797, Poland.
Pawlikowski, Marek
  • Institute of Mechanics and Printing, Warsaw University of Technology, Narbutta 85, Warsaw, 02-524, Poland.
Jankowski, Krzysztof
  • Institute of Mechanics and Printing, Warsaw University of Technology, Narbutta 85, Warsaw, 02-524, Poland.
Borowska, Marta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, Wiejska 45C, Bialystok, 15-351, Poland.
Skierbiszewska, Katarzyna
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS - SGGW), Nowoursynowska 100, Warsaw, 02-797, Poland.
Jasiński, Tomasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS - SGGW), Nowoursynowska 100, Warsaw, 02-797, Poland.
Domino, Małgorzata
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS - SGGW), Nowoursynowska 100, Warsaw, 02-797, Poland. malgorzata_domino@sggw.edu.pl.

MeSH Terms

  • Animals
  • Horses
  • Absorptiometry, Photon / veterinary
  • Bone Density
  • Algorithms
  • Horse Diseases / diagnostic imaging
  • Cluster Analysis
  • Osteoarthritis / veterinary
  • Osteoarthritis / diagnostic imaging

Conflict of Interest Statement

Declarations. Ethics approval and consent to participate: Not applicable. The research, using the samples collected postmortem at a commercial slaughterhouse, does not fall under the legislation for the protection of animals used for scientific purposes, national decree–law (Dz. U. 2015 poz. 266 and 2010–63–EU directive). No ethical approval was needed. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

References

This article includes 82 references
  1. Gillot M, Miranda F, Baquero B, Ruellas A, Gurgel M. Automatic landmark identification in cone-beam computed tomography. Orthod Craniofacial Res 2023;26:560–7.
    pmc: PMC10440369pubmed: 36811276doi: 10.1111/ocr.12642google scholar: lookup
  2. Borowska M, Jasiński T, Gierasimiuk S, Pauk J, Turek B. Three-dimensional segmentation assisted with clustering analysis for surface and volume measurements of equine incisor in multidetector computed tomography data sets. Sensors 2023;23:8940.
    pmc: PMC10650163pubmed: 37960639doi: 10.3390/s23218940google scholar: lookup
  3. Bencevic M, Galic I, Habijan M, Pižurica A. Recent progress in epicardial and pericardial adipose tissue segmentation and quantification based on deep learning: a systematic review. Appl Sci 2022;12:5217.
    doi: 10.3390/app12105217google scholar: lookup
  4. Wang H, Minnema J, Batenburg KJ, Forouzanfar T, Hu FJ, Wu G. Multiclass CBCT image segmentation for orthodontics with deep learning. J Dent Res 2021;100:943–9.
    pmc: PMC8293763pubmed: 33783247doi: 10.1177/00220345211005338google scholar: lookup
  5. Zhang C, Fan L, Zhang S, Zhao J, Gu Y. Deep learning based dental implant failure prediction from periapical and panoramic films. Quant Imaging Med Surg 2023;13:935–45.
    pmc: PMC9929426pubmed: 36819274doi: 10.21037/qims-22-457google scholar: lookup
  6. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018;77:106–11.
    pubmed: 30056118doi: 10.1016/j.jdent.2018.07.015google scholar: lookup
  7. Palanivel J, Davis D, Srinivasan D, Nc SC, Kalidass P. Artificial intelligence-creating the future in orthodontics–a review. J Evol Med Dent Sci 2021;10:2108–13.
  8. Kasban H, El–Bendary MAM, Salama DH. A comparative study of medical imaging techniques. Int J Intell Syst 2015;4:37–58.
  9. Meomartino L, Greco A, Di Giancamillo M, Brunetti A, Gnudi G. Imaging techniques in veterinary medicine. Part I: Radiography and ultrasonography. Eur J Radiol Open 2021;8:100382.
    pmc: PMC8529508pubmed: 34712745doi: 10.1016/j.ejro.2021.100382google scholar: lookup
  10. Greco A, Meomartino L, Gnudi G, Brunetti A, Di Giancamillo M. Imaging techniques in veterinary medicine. Part II: Computed tomography, magnetic resonance imaging, nuclear medicine. Eur J Radiol Open 2023;10:100467.
    pmc: PMC9768321pubmed: 36570419doi: 10.1016/j.ejro.2022.100467google scholar: lookup
  11. Leschka S, Alkadhi H, Wildermuth S, Marince B. Multi–detector computed tomography of acute abdomen. Europ Radiol 2005;15:2435–47.
    pubmed: 16132914doi: 10.1007/s00330-005-2897-4google scholar: lookup
  12. Singh NK, Raza K. Progress in deep learning-based dental and maxillofacial image analysis: a systematic review. Expert Syst Appl 2022;199:116968.
    doi: 10.1016/j.eswa.2022.116968google scholar: lookup
  13. Urban R, Haluzová S, Strunga M, Surovková J, Lifková M. AI–assisted CBCT data management in modern dental practice: benefits, limitations and innovations. Electronics 2023;12:1710.
    doi: 10.3390/electronics12071710google scholar: lookup
  14. Suh MS, Park SH, Kin YK, Yun PY, Lee WW. 18F–NaF PET/CT for the evaluation of temporomandibular joint disorder. Clin Radiol 2018;73:1–7.
    pubmed: 29223613doi: 10.1016/j.crad.2017.11.008google scholar: lookup
  15. Spriet M, Espinosa P, Kyme AZ, Phillips KL, Katzman SA. 18F–sodium fluoride positron emission tomography of the equine distal limb: exploratory study in three horses. Equine Vet J 2018;50:125–32.
    pubmed: 28707737doi: 10.1111/evj.12719google scholar: lookup
  16. Norvall A, Spriet M, Espinosa P, Arino-Estrada G, Murphy BG. Chondrosesamoidean ligament enthesopathy: prevalence and findings in a population of lame horses imaged with positron emission tomography. Equine Vet J 2021;53:451–9.
    pubmed: 32491220doi: 10.1111/evj.13299google scholar: lookup
  17. Drees R, Forrest LJ, Chappell R. Comparison of computed tomography and magnetic resonance imaging for the evaluation of canine intranasal neoplasia comparative study. J Small Anim Pract 2009;50:334–40.
    pmc: PMC2706309pubmed: 19508490doi: 10.1111/j.1748-5827.2009.00729.xgoogle scholar: lookup
  18. Karkkainen M, Punto LU, Tulamo RM. Magnetic resonance imaging of canine degenerative lumbar spine diseases. Vet Radiol Ultrasound 1993;34:399–404.
    doi: 10.1111/j.1740-8261.1993.tb02028.xgoogle scholar: lookup
  19. Tucker RL, Farrell E. Computed tomography and magnetic resonance imaging of the equine head. Vet Clin N Am Equine Pract 2001;17:131–44.
    pubmed: 11488040doi: 10.1111/j.1740-8261.1993.tb02028.xgoogle scholar: lookup
  20. Kozłowska N, Wierzbicka M, Jasiński T, Domino M. Advances in the diagnosis of equine respiratory diseases: a review of novel imaging and functional techniques. Animals 2022;12:381.
    pmc: PMC8833607pubmed: 35158704doi: 10.3390/ani12030381google scholar: lookup
  21. Tessier C, Brühschwein A, Lang J, Konar M, Wilke M. Magnetic resonance imaging features of sinonasal disorders in horses. Vet Radiol Ultrasound 2013;54:54–60.
    pubmed: 22994147doi: 10.1111/j.1740-8261.2012.01975.xgoogle scholar: lookup
  22. Manso-Díaz G, García-López JM, Maranda L, Taeymans O. The role of head computed tomography in equine practice. Equine Vet Educ 2015;27:136–45.
    doi: 10.1111/eve.12275google scholar: lookup
  23. Gutierrez-Nibeyro SD, Werpy NM, Gold SJ, Olguin S, Schaeffer DJ. Standing MRI lesions of the distal interphalangeal joint and podotrochlear apparatus occur with a high frequency in warmblood horses. Vet Radiol Ultrasound 2020;61:336–45.
    pubmed: 32162431doi: 10.1111/vru.12855google scholar: lookup
  24. Lin ST, Bolas NM, Sargan DR, Restif O, Peter VG. Comparison of cone–beam and fan‐beam computed tomography and low-field magnetic resonance imaging for detection of proximal phalanx dorsoproximal osteochondral defects. Equine Vet J 2023:1–10.
    pubmed: 37488678doi: 10.1111/evj.13973google scholar: lookup
  25. Murray RC, Mair TS, Sherlock CE, Blunden AS. Comparison of high–field and low–field magnetic resonance images of cadaver limbs of horses. Vet Rec 2009;165:281–8.
    pubmed: 19734560doi: 10.1136/vr.165.10.281google scholar: lookup
  26. Nelson BB, Kawcak CE, Barrett MF, McIlwraith CW, Grinstaff MW, Goodrich LR. Recent advances in articular cartilage evaluation using computed tomography and magnetic resonance imaging. Equine Vet J 2018;50:564–79.
    pubmed: 29344988doi: 10.1111/evj.12808google scholar: lookup
  27. Gosangi B, Mandell JC, Weaver MJ, Uyeda JW, Smith SE. Bone marrow edema at dual–energy CT: a game changer in the emergency department. Radiographics 2020;40:859–74.
    pubmed: 32364883doi: 10.1148/rg.2020190173google scholar: lookup
  28. Nagy A, Boros K, Dyson S. Magnetic resonance imaging, computed tomographic and radiographic findings in the metacarpophalangeal joints of 40 non-lame thoroughbred yearlings. Animals 2023;13:3466.
    pmc: PMC10668665pubmed: 38003084doi: 10.3390/ani13223466google scholar: lookup
  29. Hall A, Riley I. Advanced equine diagnostics–developments in computed tomography. UK–Vet Equine 2021;5:254–64.
    doi: 10.12968/ukve.2021.5.6.254google scholar: lookup
  30. Brounts SH, Henry T, Lund JR, Whitton RC, Ergun DL, Muir P. Use of a novel helical fan beam imaging system for computed tomography of the head and neck in sedated standing horses: 120 cases (2019–2020). J Am Vet Med Assoc 2022;260:1361–8.
    pubmed: 35943950doi: 10.2460/javma.21.10.0471google scholar: lookup
  31. Nagy A, Dyson S. Magnetic resonance imaging, computed tomographic and radiographic findings in the metacarpophalangeal joints of 31 warmblood showjumpers in full work and competing regularly. Animals 2024;14:1417.
    pmc: PMC11117305pubmed: 38791635doi: 10.3390/ani14101417google scholar: lookup
  32. Santos MM, Martinez J, Mollenhauer L, Schulze-Gronover B, Lescun TB, Gudehus HT. Surgical treatment of cervical (C7–T1) instability caused by discospondylitis in a horse. Equine Vet Edu 2023;35:e731–7.
    doi: 10.1111/eve.13851google scholar: lookup
  33. Baudisch N, Singer E, Jensen KC, Eichler F, Meyer HJ. Influence of surgical intervention at the level of the dorsal spinous processes on the biomechanics of the equine thoracolumbar spine. Equine Vet. J. 2024:1–10.
    pmc: PMC11807938pubmed: 38934728doi: 10.1111/evj.14123google scholar: lookup
  34. Baudisch N, Schneidewind L, Becke S, Keller M, Overhoff M. Computed tomographic study analysing functional biomechanics in the thoracolumbar spine of horses with and without spinal pathology. Anat Histol Embryol 2024;53:e13016.
    pubmed: 38230834doi: 10.1111/ahe.13016google scholar: lookup
  35. Van Zadelhoff C, Liuti T, Dixon PM, Reardon RJ. Multidetector CT and cone–beam CT have substantial agreement in detecting dental and sinus abnormalities in equine cadaver heads. Vet Rad Ultrasound 2021;62:413–20.
    pubmed: 33987964doi: 10.1111/vru.12978google scholar: lookup
  36. Ohlerth S, Scharf G. Computed tomography in small animals–basic principles and state of the art applications. Vet J 2007;173:254–71.
    pubmed: 16516508doi: 10.1016/j.tvjl.2005.12.014google scholar: lookup
  37. Bonecka J, Turek B, Jankowski K, Borowska M, Jasiński T. Selection of X–ray tube settings for relative bone density quantification in the knee joint of cats using computed digital absorptiometry. Sensors 2024;24:5774.
    pmc: PMC11398042pubmed: 39275686doi: 10.3390/s24175774google scholar: lookup
  38. Bonecka J, Turek B, Jankowski K, Borowska M, Jasiński T. Relationship between feline knee joint osteoarthritis and bone mineral density quantified using computed tomography and computed digital absorptiometry. Animals 2024;14:2615.
    pmc: PMC11394321pubmed: 39272400doi: 10.3390/ani14172615google scholar: lookup
  39. Górski K, Borowska M, Turek B, Pawlikowski M, Jankowski K. An application of the density standard and scaled–pixel–counting protocol to assess the radiodensity of equine incisor teeth affected by resorption and hypercementosis: preliminary advancement in dental radiography. BMC Vet Res 2023;19:1–15.
    pmc: PMC10413604pubmed: 37559089doi: 10.1186/s12917-023-03675-4google scholar: lookup
  40. Turek B, Borowska M, Jankowski K, Skierbiszewska K, Pawlikowski M. A preliminary protocol of radiographic image processing for quantifying the severity of equine osteoarthritis in the field: a model of bone spavin. Appl Sci 2024;14:5498.
    doi: 10.3390/app14135498google scholar: lookup
  41. Yamada K, Sato F, Higuchi T, Nishihara K, Kayano M, Sasaki N, Nambo Y. Experimental investigation of bone mineral density in Thoroughbreds using quantitative computed tomography. J Equine Sci 2015;26:81–7.
    pmc: PMC4591414pubmed: 26435681doi: 10.1294/jes.26.81google scholar: lookup
  42. Firth EC, Rogers CW. Musculoskeletal responses of 2–year–old Thoroughbred horses to early training. 7. Bone and articular cartilage response in the carpus. N Z Vet J 2005;53:113–22.
    pubmed: 15846395doi: 10.1080/00480169.2005.36488google scholar: lookup
  43. Rajão MD, Leite CS, Nogueira K, Godoy RF, Lima EMM. The bone response in endurance long distance horse. Open Vet J 2019;9:58–64.
    pmc: PMC6500865pubmed: 31086768doi: 10.4314/ovj.v9i1.11google scholar: lookup
  44. Kamran K, Rashid I, Mozhdeh E, Mohd Z, Tengku AI. Osteoporosis and bone health. JAVA 2010;9:1048–54.
    doi: 10.3923/javaa.2010.1048.1054google scholar: lookup
  45. Cresswell EN, McDonough SP, Palmer SE, Hernandez CJ, Reesink HL. Can quantitative computed tomography detect bone morphological changes associated with catastrophic proximal sesamoid bone fracture in Thoroughbred racehorses?. Equine Vet J 2019;51:123–30.
    pubmed: 29758110doi: 10.1111/evj.12965google scholar: lookup
  46. Lepage OM, Carstanjen B, Uebelhart D. Non–invasive assessment of equine bone: an update. Vet J 2001;161:10–22.
    pubmed: 11145827doi: 10.1053/tvjl.2000.0541google scholar: lookup
  47. McCarthy RN, Jeffcott LB. Effects of treadmill exercise on cortical bone in the third metacarpus of young horses. Res Vet Sci 1992;52:28–37.
    pubmed: 1553435doi: 10.1016/0034-5288(92)90054-6google scholar: lookup
  48. Bell RA, Nielsen BD, Waite K, Rosenstein D, Orth M. Daily access to pasture turnout prevents loss of mineral in the third metacarpus of Arabian weanlings. J Anim Sci 2001;79:1142–50.
    pubmed: 11374532doi: 10.2527/2001.7951142xgoogle scholar: lookup
  49. Vaccaro C, Busetto R, Bernardini D, Anselmi C, Zotti A. Accuracy and precision of computer–assisted analysis of bone density via conventional and digital radiography in relation to dual–energy x–ray absorptiometry. Am J Vet Res 2012;73:381–4.
    pubmed: 22369530doi: 10.2460/ajvr.73.3.381google scholar: lookup
  50. Bowen AJ, Burd MA, Craig JJ, Craig M. Radiographic calibration for analysis of bone mineral density of the equine third metacarpal bone. J Equine Vet Sci 2013;33:1131–5.
    doi: 10.1016/j.jevs.2013.04.016google scholar: lookup
  51. McClure SR, Glickman LT, Glickman NW, Weaver CM. Evaluation of dual energy x–ray absorptiometry for in situ measurement of bone mineral density of equine metacarpi. Am J Vet Res 2001;62:752–6.
    pubmed: 11341398doi: 10.2460/ajvr.2001.62.752google scholar: lookup
  52. El Maghraoui A, Roux C. DXA scanning in clinical practice. QJM 2008;101:605–17.
    pubmed: 18334497doi: 10.1093/qjmed/hcn022google scholar: lookup
  53. Johnson TR. Dual–energy CT: general principles. AJR 2012;199:S3–8.
    pubmed: 23097165doi: 10.2214/ajr.12.9116google scholar: lookup
  54. Ulivieri FM, Rinaudo L. Beyond bone mineral density: a new dual X–ray absorptiometry index of bone strength to predict fragility fractures, the bone strain index. Front Med 2021;7:590139.
    pmc: PMC7843921pubmed: 33521014doi: 10.3389/fmed.2020.590139google scholar: lookup
  55. Waite K, Nielsen BD, Rosenstein DS. Computed tomography as a method of estimating bone mineral content in horses. J Equine Vet Sci 2000;20:49–52.
    doi: 10.1016/s0737-0806(00)80321-4google scholar: lookup
  56. Kobayashi M, Ando K, Kaneko M, Inoue Y, Asai Y, Taniyama H. Measurement of equine bone mineral content by radiographic absorptiometry using CR and ortho systems. J Equine Sci 2006;17:105–12.
    doi: 10.1294/jes.17.105google scholar: lookup
  57. Kobayashi M, Ando K, Kaneko M, Inoue Y, Asai Y, Taniyama H. Clinical usefulness of the measurement of bone mineral content by radiographic absorptiometry in the young thoroughbred. J Equine Sci 2007;18:99–106.
    doi: 10.1294/jes.18.99google scholar: lookup
  58. Shipley T, Farouk K, El–Bialy T. Effect of high–frequency vibration on orthodontic tooth movement and bone density. J Orthod Sci 2019;8:15.
    pmc: PMC6702681pubmed: 31497574doi: 10.4103/jos.jos_17_19google scholar: lookup
  59. Hounsfield GN. Nobel Award address. Computed medical imaging. Med Phys 1980;7:283–90.
    pubmed: 6993911doi: 10.1118/1.594709google scholar: lookup
  60. Junior TAA, Nogueira MS, Vivolo V, Potiens MPA, Campos LL. Mass attenuation coefficients of X–rays in different barite concrete used in radiation protection as shielding against ionizing radiation. Radiat Phys Chem 2017;140:349–54.
    doi: 10.1016/j.radphyschem.2017.02.054google scholar: lookup
  61. Borowska M, Turek B, Lipowicz P, Jasiński T, Skierbiszewska K, Domino M. An application of the scaled–pixel–counting protocol to quantify the radiological features of anatomical structures of the normal tarsal joint in horses. Acta Mech Autom 2024;18:483–9.
    doi: 10.2478/ama-2024-0051google scholar: lookup
  62. Ghazal TM. Performances of K-means clustering algorithm with different distance metrics. Intell Autom Soft Comput 2021;30:735–42.
    doi: 10.32604/iasc.2021.019067google scholar: lookup
  63. Borowska M, Lipowicz P, Daunoravičienė K, Turek B, Jasiński T. Three-dimensional segmentation of equine paranasal sinuses in multidetector computed tomography datasets: preliminary morphometric assessment assisted with clustering analysis. Sensors 2024;24:3538.
    pmc: PMC11175080pubmed: 38894336doi: 10.3390/s24113538google scholar: lookup
  64. Belhassen S, Zaidi H. A novel fuzzy C–means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys 2010;37:1309–24.
    pubmed: 20384268doi: 10.1118/1.3301610google scholar: lookup
  65. Zukotynski K, Black SE, Kuo PH, Bhan A, Adamo S. Exploratory assessment of K–means clustering to classify 18F–flutemetamol brain PET as positive or negative. Clin Nucl Med 2021;46:616–20.
    pubmed: 33883495doi: 10.1097/rlu.0000000000003668google scholar: lookup
  66. Moftah HM, Azar AT, Al–Shammari ET, Ghali NI, Hassanien AE, Shoman M. Adaptive k–means clustering algorithm for MR breast image segmentation. Neural Comput Appl 2014;24:1917–28.
    doi: 10.1007/s00521-013-1437-4google scholar: lookup
  67. Alam MS, Rahman MM, Hossain MA, Islam MK, Ahmed KM. Automatic human brain tumor detection in MRI image using template–based K means and improved fuzzy C means clustering algorithm. Big Data Cogn Comput 2019;3:27.
    doi: 10.3390/bdcc3020027google scholar: lookup
  68. Valentinitsch A, Trebeschi S, Kaesmacher J, Lorenz C, Löffler MT. Opportunistic osteoporosis screening in multi–detector CT images via local classification of textures. Osteoporos Int 2019;30:1275–85.
    pmc: PMC6546649pubmed: 30830261doi: 10.1007/s00198-019-04910-1google scholar: lookup
  69. Cho J, Ryu S, Jang HJ, Park JY, Ha Y. Clinical effect of transverse process hook with K-means clustering-based stratification of computed tomography Hounsfield unit at upper instrumented vertebra level in adult spinal deformity patients. J Korean Neurosurg Soc 2023;66:44–52.
    pmc: PMC9837488pubmed: 36050868doi: 10.3340/jkns.2022.0174google scholar: lookup
  70. https://scikit-learn.org/stable/index.html. Access 02 Nov 2023.
  71. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#. Access 02 Nov 2023.
  72. https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#. Access 02 Nov 2023.
  73. https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#. Access 02 Nov 2023.
  74. https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics. Access 02 Nov 2023.
  75. Schaer TP, Bramlage LR, Embertson RM, Hance S. Proximal interphalangeal arthrodesis in 22 horses. Equine Vet J 2001;33:360–5.
    pubmed: 11469768doi: 10.2746/042516401776249552google scholar: lookup
  76. Bonecka J, Skibniewski M, Zep P, Domino M. Knee joint osteoarthritis in overweight cats: the clinical and radiographic findings. Animals 2023;13:2427.
    pmc: PMC10417339pubmed: 37570234doi: 10.3390/ani13152427google scholar: lookup
  77. Homolka P, Nowotny R. Production of phantom materials using polymer powder sintering under vacuum. Phys Med Biol 2002;47:N47–52.
    pubmed: 11848127doi: 10.1088/0031-9155/47/3/401google scholar: lookup
  78. Morgan JP, Wolvekamp P. Atlas of radiology of the traumatized dog and cat: the case–based approach. 2nd ed. Magdeburg: Schlütersche; 2010.
  79. Ranner W, Schill W, Gerhards H. Radiologic examination of the spine in “back problems” of the standing horse. Tierarztl Prax Ausg G Grosstiere Nutztiere 1999;27:122–7.
    pubmed: 10326239
  80. Alawi M, Begum A, Harraz M, Alawi H, Bamagos S. Dual–energy X–ray absorptiometry (DEXA) scan versus computed tomography for bone density assessment. Cureus 2021;13:e13261.
    pmc: PMC7954087pubmed: 33717764doi: 10.7759/cureus.13261google scholar: lookup
  81. Ley CJ, Björnsdóttir S, Ekman S, Boyde A, Hansson K. Detection of early osteoarthritis in the centrodistal joints of Icelandic horses: Evaluation of radiography and low-field magnetic resonance imaging. Equine Vet J 2016;48:57–64.
    pubmed: 25290785doi: 10.1111/evj.12370google scholar: lookup
  82. Sloet van Oldruitenborgh–Oosterbaan MM, Genzel W, Van Weeren PR. A pilot study on factors influencing the career of Dutch sport horses. Equine Vet J 2010;42:28–32.
    pubmed: 21058979doi: 10.1111/j.2042-3306.2010.00251.xgoogle scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.