An investigation of segmentation methods and texture analysis applied to tomographic images of human vertebral cancellous bone.

The goal of this study is to determine architectural and textural parameters on computed tomographic (CT) images, allowing us to explain the mechanical compressive properties of bone. Although the resolution (150 microm) is of the same order of magnitude as the trabecular thickness, this method enables the possibility of perfecting an in vivo peripheral CT system with an acceptable radiation dose for the patient. This study was performed on L2 vertebrae cancellous bone specimens taken after necropsy in 22 subjects aged 47-95 years (mean: 79 years). The segmentation process is a crucial point in the determination of accurate architectural parameters. In this paper the use of two different segmentation methods is investigated, based on an edge enhancement and a region growing approach. The images are compared and the architectural parameters extracted from the images segmented by both methods lead to a quantitative evaluation. The parameters are found to be globally robust towards the segmentation process, although some of them are much more sensitive to the approach used. Highly significant correlations (P < 0.0005) have been obtained between the two segmentation methods for all the parameters, with rho ranging from 0.70 to 0.93. In order to improve the assessment of bone architecture, texture analysis (run length method) was investigated. New features are obtained from an image reduced to 16 grey-levels. Textural parameters in addition to architectural parameters in a multivariate regression model increase significantly (P = 0.01) the prediction of the maximum compressive strength (variation of r2 from 0.75 up to 0.89).