A Semi Segmentation Of Knee Articular Cartilage Using Varitional Methods

Osteoarthritis (OA) is a syndrome of joint pain that affects the large weight bearing joints. It is caused by an abnormal wearing of articular cartilage, covering the joints. In addition to the inconvenience OA causes, its treatment is very time consuming and expensive. Therefore it is desirable to improve methods for an early diagnosis of OA. The detection of thinning of articular cartilage provides a good support for the diagnosis of OA in its early stage. The first step in this diagnosis process is the accurate segmentation of the cartilage surface.In this Master's Thesis we propose an interactive segmentation framework for the semi automatic segmentation of articular cartilage.Until today, no automatic segmentation method is able achieve the accuracy, necessary for a trustworthy diagnosis. Also, physicians in general prefer to be able to control and modify the segmentation result, which is usually complicated using automatic methods.Semi automatic methods allow the user to incorporate knowledge into the segmentation process, whilst reducing the time and improving the repeatability compared to fully manual methods.The author in-depth study of the anatomical structure of articular cartilage based on, reviewed the various methods of image segmentation, in which the basic approach, I chose the boundary based deformable model to find ways to segment of articular cartilage. And the introduction of a separate measure of similarity:DSC, to the framework to evaluate the accuracy of segmentationThe proposed segmentation model is based on a weighted Total Variation energy and minimised using efficient numerical approaches. User programmable graphics card to use the nVidia provides CUDA framework to achieve segmentation process, allowing users real-time operation. Using three real human knee magnetic resonance imaging to evaluate the segmentation method, these three sets of data are: load data, flexible data, Dess3D data. Comparison of manual segmentation methods, our method of segmentation is improved significantly speed, and received more than 0.9 similarity, that this segmentation framework is effective segmentation.