{
  "abstract": "While gray scale density metrics are susceptible to cone beam computed tomography (CBCT) noise and calibration inconsistencies, advanced image processing algorithms offer a potential non-invasive, objective alternative. This study presents a comparative evaluation of Fractal Analysis (FA) and Canny Edge-Detection (CED) algorithms for quantifying and classifying distinct bone trabecular patterns. In this ex vivo experimental study, 120 bovine rib bone blocks were categorized into four morphologic groups (D1-D4) and imaged using a standardized CBCT protocol. A single standardized 50 × 50-pixel region of interest (ROI) was analyzed on a selected two-dimensional reconstructed slice from each block. FA and CED quantified structural complexity and edge count, respectively. Group differences were evaluated using analysis of variance with multiplicity-adjusted post hoc tests, and exploratory receiver operating characteristic (ROC) analyses were used to estimate pairwise discrimination. Both FA and CED values differed significantly across the four morphologic groups (p < 0.001). FA differentiated every pairwise comparison (p ≤ 0.014), whereas CED did not differentiate D2 from D3 (p = 0.717). Exploratory AUCs ranged from 0.77 to 0.99 for FA and from 0.64 to 1.00 for CED. Within this controlled ex vivo model, FA and CED provided objective, reproducible quantification of distinct trabecular bone patterns on CBCT images. FA excels in differentiating intermediate densities (D2 and D3), while CED offers superior performance for denser and looser bone patterns (D1 and D4). The complementary performance of the two algorithms supports their potential as quantitative adjuncts to visual bone-pattern assessment.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Oral and Maxillofacial Radiology, Dental Sciences Research Center, School of Dentistry, Guilan University of Medical Sciences",
    "Department of Periodontics, Dental Sciences Research Center, School of Dentistry, Guilan University of Medical Sciences",
    "Department of Prosthodontics, Dental Sciences Research Center, School of Dentistry, Guilan University of Medical Sciences",
    "Department of Biostatistics and Epidemiology, School of Health, Qom University of Medical Sciences"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kasra Habibi"
    },
    {
      "affs": [
        1
      ],
      "name": "Bardia Vadiati Saberi"
    },
    {
      "affs": [
        2
      ],
      "name": "Amirreza Hendi"
    },
    {
      "affs": [
        0
      ],
      "name": "Negar Khosravifard"
    },
    {
      "affs": [
        0
      ],
      "name": "Farzaneh Ostovarrad"
    },
    {
      "affs": [
        3
      ],
      "name": "Mohammad Ebrahim Ghaffari"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a0x94e813xy71.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-72893-1",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-72893-1",
  "id": "a0x94e813xy71",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-72893-1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "3504|Dental Radiography and Imaging"
    ]
  ],
  "openalex": "W7214055855",
  "pdf_url": "https://www.nature.com/articles/s41598-026-72893-1_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/a0x94e813xy71.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:53Z",
      "native_id": "89fc03afdcee44979b06463260f989b3",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-72893-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Comparative evaluation of fractal analysis and canny edge-detection algorithms for the quantification and classification of bone trabecular patterns in cone-beam computed tomography",
  "title_text": "Comparative evaluation of fractal analysis and canny edge-detection algorithms for the quantification and classification of bone trabecular patterns in cone-beam computed tomography",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:53Z",
  "url": "https://research.draconitic.com/paper/a0x94e813xy71",
  "venue": "Scientific Reports",
  "versions": []
}