Histogram feature-based classification improves differentiability of early bone healing stages from micro-computed tomographic data.
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.


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 (R = 0.73). The moment-based approach yielded a clear distinction between all 3 groups with a classification accuracy up to R = 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.
Julius Wolff Institute and Center for Musculoskeletal Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Bernd
B
Preininger
Bernhard
B
Hesse
Daniel
D
Rohrbach
Peter
P
Varga
Hinnerk
H
Gerigk
Max
M
Langer
Francoise
F
Peyrin
Carsten
C
Perka
Kay
K
Raum
D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D001842:Bone and Bones / Q000000981:diagnostic imaging* / Q000601:surgery; D010027:Osteotomy; D010865:Pilot Projects; D051381:Rats; D017207:Rats, Sprague-Dawley; D014945:Wound Healing; D055114:X-Ray Microtomography / Q000379:methods*