Articular cartilage (AC) of knee plays a vital role in the supporting mechanism of the human body. AC is a smooth, slippery surface which composed of white tissue covering the joint shell. Magnetic Resonance (MR) imaging technique is widely adopted in clinical practice to visualize and detect the defect in Knee anatomical structure. Segmentation of AC using knee MR images is an essential process to biomarker analysis during knee osteoarthritis progression. Hence it has been widely investigated by research communities. Osteoarthritis (OA) is a commonly found knee disease, in which AC is slowly and progressively lost. Early diagnosis of OA plays a significant role to look up treatment possibilities and prevent further degradation of knee AC. Segmentation methods of AC have considered various parameters of image, image quality, and tissue structure, etc. Manual segmentation of cartilage for OA diagnosis, from a large amount of MRI images produced in clinical practice, is a time-consuming and challenging process. Thus semi-automated and fully automated cartilage segmentation methods have been adopted by many researchers to provide reasonable accuracy and reliable detection of AC from knee MR images. This paper reviews knee cartilage segmentation methods from MR images. In this paper, we focus on the recent tread of knee AC segmentation techniques from MR images. First, an introduction to osteoarthritis and its causes are given. Then variety of MR imaging modalities suitable for assessment of knee OA. Finally, performance measures are explained followed by conclusion.