Infrared thermogram image guided discontinuous appearances of hyaluronic acid for classification of arthritic knee joints

The liveliness of a human potentially depends on his/her smooth movability. To accomplish the work of daily life, the joints of the body need to be healthy. However, the occurrence of Rheumatoid arthritis and Osteoarthritis has a significant prevalence towards the immovability of humankind. Rheumatoid arthritis (RA) and Osteoarthritis (OA) mostly affect the joints of the hand and knee which result in lifelong pain, inability to climb, walk, etc. In the early stages, these diseases attack the synovial membrane and synovial fluid, and further it destroys the soft tissues and bone structure. By early diagnosis, we can start the treatment in the early stage which may cure these diseases with such extreme consequences. As per clinical studies of previous literature, it is observed that synovial fluid imbalance appears in the early stage of such diseases and Hyaluronic Acid (HA) concentration also decreases for that. Therefore, estimation of HA is a significant key to arthritis disease classification and grading. In this paper, we proposed a hybrid framework for classification of arthritic knee joints based on the analysis of the discontinuous appearances of the HA concentration using infrared imaging technology. To meet up the specific necessities, firstly we have proposed a modified K-Means clustering algorithm for extraction of the region of interest (ROI) i.e., the knee joint surface. Secondly, a mathematical formulation is proposed to calculate the concentration of HA from the segmented ROIs. This experimental process was implemented on the publicly available IR (Infrared) Knee Joint Dataset and for further evaluation of the novelty of mathematical formulation, we have extended the proposed work to the classification of healthy and arthritis affected knee joints depending on significant discriminative characteristics of the HA concentration with respect to the existing significant imaging features. Experimental results and analysis demonstrates that concentration of HA has the dominant potential for classifying healthy and arthritic knee joints using infrared holistic images. Our experimental analysis reveals that estimation and combination of the HA concentration features with conventional handcrafted and deep features increases the classification performance with an average accuracy of 91% and 97.22% respectively as compared to the each individual feature sets.