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Journal of Computer Assisted Tomography:
doi: 10.1097/RCT.0b013e31825eae8a
Musculoskeletal Imaging

Histogram Feature–Based Classification Improves Differentiability of Early Bone Healing Stages From Micro-Computed Tomographic Data

Preininger, Bernd MD*; Hesse, Bernhard MSc†‡; Rohrbach, Daniel MSc*†; Varga, Peter PhD*; Gerigk, Hinnerk*; Langer, Max PhD‡§; Peyrin, Francoise PhD‡§; Perka, Carsten MD*; Raum, Kay PhD*†

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Abstract

Objective: Contrast between not fully mineralized tissues is weak and limits conventional computed tomography (CT). An automated grayscale histogram-based analysis features could improve the sensitivity to tissue alterations during early bone healing.

Materials and Methods: Tissue formation in a rat osteotomy model was analyzed using in vivo micro-CT and classified histologically (mineralized, cartilage, and connective tissues). A conventional threshold-based method including manual contouring was compared to a novel moment-based method: after removing the background peak, the histograms of each slice were characterized by their moments and analyzed as a function of the position along the long bone axis.

Results: The threshold-based method could differentiate between the mineralized and connective tissue (R2 = 0.73). The moment-based approach yielded a clear distinction between all 3 groups with a classification accuracy up to R2 = 0.93.

Conclusions: The moment-based evaluation outperforms the conventional threshold-based CT analysis in sensitivity to the healing stage, user independence, and time consumption.

© 2012 Lippincott Williams & Wilkins, Inc.

  

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