Progression Analysis and Stage Discovery in Continuous Physiological Processes Using Image Computing

Osteoarthritis (OA) is a highly prevalent chronic clinical condition that limits mobility and causes substantial disability in late life [1]. It is estimated that of the population over the age of 65 have radiographic evidence of osteoarthritis [2], and given the increasing longevity in the industrialized world the prevalence of osteoarthritis is expected to increase further in the developed countries. While OA is one of the most prevalent diseases in the industrialized world, the physiological mechanisms of OA are poorly understood [3]. Yet, due to the increasing prevalence of knee osteoarthritis and its consequent effects on functional limitation and general life quality at older ages, there is a growing need for scientific tools that can be reliably used to study the mechanisms of OA.The presence and progression of osteoarthritis is usually evaluated by trained radiologists, who read knee X-rays and score them by using the standard Kellgren-Lawrence (KL) system [4, 5]. The KL classification scheme is a validated method for classifying individual joints into one of five grades, with 0 representing healthy joints, 1 representingdoubtful OA, 2 representingmild OA, 3moderate OA, and 4 being the most severe radiographic disease. This classification is based on features ofosteophytes (bony growths adjacent to the joint space),narrowing of part or all of the tibial-femoral joint space, andsclerosis of the subchondral bone, which reflect the progression of the disease. Figure 1 shows four knee X-rays of KL grades 0 (normal), 1 (doubtful), 2 (mild), and 3 (moderate).It should be noted that while the Kellgren-Lawrence classification is the most commonly used classification scheme, there is no scientific evidence that the KL system provides an accurate direct assessment of the progression of OA [6, 7]. This downside of the KL classification scheme limits its potential as an objective tool that can be used to directly study the mechanisms and nature of OA, as well as assess