Hierarchical bayesian modeling, estimation, and sampling for multigroup shape analysis.

This paper proposes a novel method for the analysis of anatomical shapes present in biomedical image data. Motivated by the natural organization of population data into multiple groups, this paper presents a novel hierarchical generative statistical model on shapes. The proposed method represents shapes using pointsets and defines a joint distribution on the population's (i) shape variables and (ii) object-boundary data. The proposed method solves for optimal (i) point locations, (ii) correspondences, and (iii) model-parameter values as a single optimization problem. The optimization uses expectation maximization relying on a novel Markov-chain Monte-Carlo algorithm for sampling in Kendall shape space. Results on clinical brain images demonstrate advantages over the state of the art.

Fractal dimension analysis of periapical reactive bone in response to root canal treatment.

OBJECTIVE: Mathematical morphology and box counting were used to extract trabecular pattern and to evaluate changes of reactive bone following root canal treatment. STUDY DESIGN: Periapical radiographs were digitized and processed with mathematical morphology operations known as skeletonization. The trabecular patterns resulting from this skeletonization process were further analyzed with fractal dimension (FD) analysis using the box-counting method. Two groups of regions of interest (ROI) were selected from 19 subjects for the analysis. RESULTS: Seventeen patients in one group and 13 patients in the other showed decreased FD in the reactive bone region after clinically successful root canal treatment (RCT). Significant changes in FD were noted 6 months after RCT (P < .05). Kappa analysis indicated significant reproducibility between the 2 groups of ROIs. CONCLUSIONS: Mathematical morphology combined with box counting showed decrease of FD in reactive bone regions after clinically successful endodontic treatment.