Segmentation is one of important methods in medical images processing, particularly as it allows images to be analysed. The method used for segmentation depends on the image problem to be resolved. In this research, knee cartilage needs to be segmented to determine the level of the Osteoarthritis (OA) and for further treatment. Knee cartilage is a soft hyline sponge that is located at the end of the femur, tibia and patella bone to release friction during movement. OA is a knee cartilage problem wherein there is a thinning of the cartilage that results in a shift especially happening between femur and tibia bone causing discomfort and pain. Thinning of the knee cartilage is due to many factors such as age, body weight, genetic, accident, sport injury and extreme use such as physical work. OA can occur to a male or female, child or adult. The effects experienced by patients with OA are such as difficulty to walk, limited movement, and pain in the thin cartilage areas. Monitoring of patients' condition needs to be done to help reduce the problem and thereby enable specialists to perform the appropriate treatment. Imaging is a method used today to monitor the condition of patients with OA. Previous studies showed that MRI is a suitable method for monitoring the condition of patients with OA because of its advantages in visualising knee cartilage more clearly than other imaging methods. Thus, for segmenting the knee cartilage which as mentioned before is an important process in medical images processing, the MR images were selected based on many factors. Segmentation in this study was aimed to obtain the cartilage region to diagnose patient OA level. Various segmentation techniques have been developed by researchers in segmenting the knee cartilage region but they have been unable to segment precisely due to the thin structure of the knee cartilage, especially for patients with intermediate and severe OA. COMSeg technique was developed to segment knee cartilage, especially for those experiencing a normal and intermediate OA and try to implement it to severe OA. The development of this new technique takes into account the imaging method used, the images feature obtained so it can be suitable to process knee image and then selection of an appropriate technique to be applied to the selected images as input.
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