ACL and PCL of the knee joint in the computer diagnostics

The essential aim of the presented studies is the feature vector of the anterior and posterior cruciate ligaments (ACL and PCL) finding. The feature vector must satisfy mandatory two tasks. The first one has to clearly define the ligaments structure and the second has to make easier diagnose their. The feature vector finding is based on the successive steps in extraction process of both ACL and PCL. In the first stage a region of interest including cruciate ligaments (CLs) is outlined. The automatic method of location of the CL on the T1-weighted (T1W) MRI (magnetic resonance imaging) knee images is based on fuzzy C-means (FCM) algorithm with median modification. The next step of that process is an extraction of the cruciate ligament structure using the fuzzy connectedness approach. In the last stage the feature vector is built. The most important criterium in the CLs diagnosis are the surface area and shape CL structures. Therefore the feature vector has to include the surface area and skeleton of the segmented and extracted structures of the cruciate ligaments.