A Fully Automated Segmentation of Knee Bones and Cartilage Using Shape Context and Active Shape Models

In this master’s thesis a fully automated method is presented for seg-menting bones and cartilage in magnetic resonance imaging (MRI) of the knee. The knee joint is the most complex joint in the human body and supports the weight of the whole body. This complexity and acute task of the knee joint leads to a disabling disease called Osteoarthritis among the adult population. The disease leads to loss of cartilage and torn cartilage cannot be repaired unless surgical techniques are used. Therefore, one of the important parts of finding the disease and planning the knee surgery is to segment bones and cartilages in MRI. The segmentation method is based on Statistical Shape Model (SSM) and Active Shape Model (ASM) built from a MICCAI 2010 Grand chal-lenge training database. First, all the data are represented by points and faces. A Shape context algorithm is applied on 60 data sets to obtain consistent landmarks. The mentioned consistent landmarks and Princi