A unified method based on wavelet filtering and Active Contour Models for segmentation of Pelvic CT images

Accurate segmentation of bone tissue from Pelvic CT images is an important step in the process of developing an automated computer aided decision making system that would provide physicians with recommendations for the diagnosis and treatment of traumatic pelvic injuries. The proposed algorithm is an automated, unsupervised, and hierarchical method for the segmentation of bone tissue. The method incorporates, as key components, wavelet processing, automated seed growing and Active Contour Models (ACM´s). A wavelet based method is applied for filtering and enhancing of noisy CT images that are the target of segmentation. The main task of the proposed seed growing is to automatically find a suitable set of points for ACM initialization. Another benefit of the proposed method is that the resulting seeds are suitable for identifying small fragments of shattered bones. ACM is used to capture the edges of larger bones that, due to their natural varying densities, and consequently varying grey levels, cannot be correctly segmented by solely using seed growing. The preliminary results produced by the proposed method are very promising. The proposed method performs the challenging task of identifying the fragments of fractured bone, as well as accurately detecting the edges of bones in the pelvic region. Moreover, separation between bones is identified even in challenging areas such as hip joints.