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.

Non-invasive detection technologies of solid foreign matter and their applications to lyophilized pharmaceutical products: A review

Good Manufacturing Practice Regulations, under the Food and Drug Administration (FDA), stipulate that all pharmaceutical products must be free of any contaminants, including, namely, any foreign solid objects. Lyophilization is a common manufacturing method that consists of several steps where foreign materials may enter the product. The presence of unintended particles in freeze drying, which will herein be referred to under the term 'Lyophilization', is of great concern to the authorities responsible for drug safety and effectiveness. In the pharmaceutical industry, presently, the inspection of lyophilized products for foreign matter particulates relies on visual inspection where only the outer surface of the lyophilized cake is visible. This review is motivated by the need for new control strategies for foreign matter (FM) detection in lyophilized products; more specifically, it assesses the reliability of non-destructive technologies for FM detection in dried samples. Emerging technologies applied in other industries, such as various types of spectroscopies and imaging (e.g. chemical, X-ray, ultrasound, thermal and terahertz), are evaluated based on compatibility with the intended application, with identification of the possible technical challenges.

Data Tagging in Medical Images: A Survey of the State-of-Art

A huge amount of medical data is generated every second, and a significant percentage of the data are images that need to be analyzed and processed. One of the key challenges in this regard is the recovery of the data of medical images. The medical image recovery procedure should be done automatically by the computers that are the method of identifying object concepts and assigning homologous tags to them. To discover the hidden concepts in the medical images, the lowlevel characteristics should be used to achieve high-level concepts and that is a challenging task. In any specific case, it requires human involvement to determine the significance of the image. To allow machine-based reasoning on the medical evidence collected, the data must be accompanied by additional interpretive semantics; a change from a pure data-intensive methodology to a model of evidence rich in semantics. In this state-of-art, data tagging methods related to medical images are surveyed which is an important aspect for the recognition of a huge number of medical images. Different types of tags related to the medical image, prerequisites of medical data tagging, different techniques to develop medical image tags, different medical image tagging algorithms and different tools that are used to create the tags are discussed in this paper. The aim of this state-of-art paper is to produce a summary and a set of guidelines for using the tags for the identification of medical images and to identify the challenges and future research directions of tagging medical images.