Abstract
This paper presents a new strategy for the segmentation of trabecular bone image. This kind of image is acquired with microcomputed tomography (micro-CT) to assess bone microarchitecture based chiefly on bone mineral density (BMD) measurements to improve fracture risk prediction. Disease osteoporosis can be predicted from features of CT image where a bone region may consist of several disjoint pieces. It relies on a multiresolution representation of the image by the wavelet transform to compute the multiscale morphological gradient. The coefficients of detail found at the different scales are used to determine the markers and homogeneous regions that are extracted with the watershed algorithm. The method reduces the tendency of the watershed algorithm to oversegment and results in closed homogeneous regions. The performance of the proposed segmentation scheme is presented via experimental results obtained with a broad series of images.
Highlights
Osteoporosis is a metabolic bone disease [1]
The disease is defined by low bone mass and microarchitectural deterioration of bone tissues leading to enhanced bone fragility
The exact clinical estimation of bone strength and fracture risk is important for the treatment of bone diseases such as osteoporosis
Summary
Osteoporosis is a metabolic bone disease [1]. The disease is defined by low bone mass and microarchitectural deterioration of bone tissues leading to enhanced bone fragility. The exact clinical estimation of bone strength and fracture risk is important for the treatment of bone diseases such as osteoporosis It designates a major health problem in industrialized countries. Throughout acquisition, it is required to establish a high number of parameters that result in the presence of noise, nonhomogeneous illumination, and fuzzy contours Such segmentations can be very challenging to obtain since osseous tissue does not always produce readily discernible features from soft tissue regions in CT images. O’Callaghan and Bull [11] proposed a combined morphological-spectral unsupervised image segmentation method This used the subbands of the dualtree complex wavelet transform in order to process both textured and nontextured objects in a meaningful fashion. We propose to segment the trabecular bone images using a watershed method with a multiresolution analysis to compute a gradient recognition and controlled markers. Reconstructed gradient image Figure 3: Overall scheme of the reconstructed gradient image
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