On digital distance transforms in three dimensions
Computer Vision and Image Understanding
A new minimum variance region growing algorithm for image segmentation
Pattern Recognition Letters
Automated 3D region growing algorithm based on an assessment function
Pattern Recognition Letters
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In view of the great effects of osteoporosis on public health, it would be of great value to be able to measure the three-dimensional structure of trabecular bone in vivo as a means to diagnose and quantify the disease. The aim of this work was to implement a method for quantitative characterisation of trabecular bone structure using clinical CT. Several previously described parameters have been calculated from volumes acquired with a 64-slice clinical scanner. Using automated region growing, distance transforms and three-dimensional thinning, measures describing the number, thickness and spacing of bone trabeculae was obtained. Fifteen bone biopsies were analysed. The results were evaluated using micro-CT as reference. For most parameters studied, the absolute values did not agree well with the reference method, but several parameters were closely correlated with the reference method. The shortcomings appear to be due to the low resolution and high noise level. However, the high correlation found between clinical CT and micro-CT measurements suggest that it might be possible to monitor changes in the trabecular structure in vivo.