Multi-surface, multi-object optimal image segmentation: Application in three-dimensional knee joint imaged by MR

A novel method called LOGISMOS -- Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces -- for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects is reported. The approach is based on representation of the multiple inter-relationships in a single n-dimensional graph, followed by graph optimization that yields a globally optimal solution.;Three major contributions for LOGISMOS are made and illustrated in this thesis: (1) multi-object multi-surface optimal surface detection graph design, (2) implementation of a novel and reliable cross-object surface mapping technique, and (3) pattern recognition-based graph cost design.;The LOGISMOS method's utility and performance are demonstrated on a kneejoint bone and cartilage segmentation task. Although trained on only a small number of nine example images, this system achieved good performance as judged by Dice Similarity Coefficients (DSC) using a leave-one-out test, with DSC values of 0.84 +/- 0.04, 0.80+/-0.04 and 0.80+/-0.04 for the femoral, tibial, and patellar cartilage regions, respectively. These are excellent values of DSC considering the narrow-sheet character of the cartilage regions. Similarly, very low signed mean cartilage thickness errors were observed when compared to manually-traced independent standard in 60 randomly selected 3D MR image datasets from the Osteoarthritis Initiative database -- 0.11 +/- 0.24, 0.05 +/- 0.23, and 0.03 +/- 0.17 mm for the femoral, tibial, and patellar cartilage thickness, respectively. The average signed surface positioning error for the 6 detected surfaces ranged from 0.04+/-0.12 mm to 0.16+/-0.22 mm, while the unsigned surface positioning error ranged from 0.22+/-0.07 mm to 0.53+/-0.14 mm.;The reported LOGISMOS framework provides robust and accurate segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multi-object multi-surface segmentation problems.