Detection of Septic Arthritis using Meta Heuristic Algorithms Jobin Christ MC 1* , Lakshmi Narayanan A 1 and Rahul Krishnan 2 1 Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, India 2 Department of Electronics & Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India * Corresponding author: Jobin Christ MC, Professor, Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, India, Tel: 044 3718 1111; E- mail: jobinchrist@gmail.com Received date: November 14, 2017; Accepted date: November 21, 2017; Published date: November 25, 2017 Copyright: © 2017 Christ MCB, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Arthritis is one of the kinds of chronic disease which causes inflammation to the joints which causing pain and stiffness that can worsen with aging. Mostly it is caused due to the decrease of Cartilage thickness present between the bone joints. There are more than 100 types of arthritis exist in the world. In general arthritis is classified into two parts, septic arthritis and reactive arthritis. Septic arthritis is an unfavorable arthropathy originated by an intra- articular infection which is usually connected to severe symptoms such as pain and decreased the range of motion. In this work, an analysis has been done on two meta-heuristic methods for early detection of septic arthritis since it is a direct invasion of bacteria. In this paper two different meta-heuristic methods like Ant Colony Optimization (ACO) and Clown Fish Queuing and Switching Optimization Algorithm (CFQSOA) are analyzed for the early detection of septic arthritis. By the early diagnosis and treatment of arthritis, the damage to the joints can be reduced. Keywords: Arthritis; Septic Arthritis; Meta Heuristic Algorithms; Image Processing Introduction Arthritis is the common disease among elderly people that usually happens between the bone joints where the cartilages are present. Due to the continuous usage of the joints, this cartilage will be reduced gradually, and at a certain level, it will be disappeared. Ten the bones will encounter each other which results in severe pain. Arthritis causes deformation in joints and leads to incapacitation in motion. Septic arthritis is a kind of painful infection of a joint. Te infection can occur from germs that travel through your bloodstream from another part of your body. Tere is a chance to occur Septic arthritis when a penetrating injury supplies germs straight into the joint. Infants and older adults are usually feasible to develop septic arthritis. Knees are the hotspot for arthritis, but septic arthritis also can afect hips, shoulders and other joints. Te infection can spread and severely afect the cartilage and bone inside the joint, so prompt treatment is very important. Treatment involves draining the joint with a needle or surgically [1]. Te diagnosis of joint sepsis is mostly straightforward because the victim ofen presents with a painful joint, purulent synovial fuid and sufered by fever. Te main infuence that contributes to septic arthritis comprises bacteraemia, old age, prosthetic joints, an immune compromised state and intra-articular injections. Imaging generally plays an adjunct role to arthrocentesis in the diagnosis of joint sepsis. If synovial fuid cannot be retrieved, it cannot be reverted and causes impairment in motion. Tus early detection of arthritis may help to the reduction of the damage to the joints [2]. Here, radiology plays a major role here. X-ray, CT, MRI and Ultrasound methods are used commonly for the early diagnosis of septic arthritis. However various image processing algorithms will help the radiologists to diagnose septic arthritis in a faster manner. In this work two diferent meta-heuristic methods such as Ant Colony Optimization (ACO) and Clown Fish Queuing and Switching Optimization Algorithm (CFQSOA) are analyzed for the early detection of septic arthritis. Te use of meta-heuristics has signifcantly increased the ability of fnding very high-quality solutions to hard, practically relevant combinatorial optimization problems in a reasonable time [3]. Ant Colony Optimization Ant Colony Optimization (ACO) is a popular population based optimization method [4]. ACO is based on the inspiration of real ant colony and their combined summing behavior. Real ants are having the capacity of fnding the shortest route from a food source to their nest without using visual indications. In general ants will move on a straight line that connects the food source to their nest is a pheromone trail [5]. Pheromone is a volatile chemical substance produced by ants while moving, and each ant probabilistically prefers to follow a direction rich in pheromone [6]. Tis behavioral aspect of the real ants may be helpful to obtain optimized value from a population. In ACO, each and every ant builds a part of the solution using an artifcial pheromone, the pheromone which is having high value that will infuence in the results. Clown Fish Queuing and Switching Optimization Algorithm Tis algorithm is based on the clown fsh’s queuing characteristics. Te clown fsh is a type of fsh; they attach among themselves and also survive symbiotically with the other sea anemones. Te clown fsh’s ecological mannerisms are to obtain the social rank in the foremost position in the shorter queues in sea anemone. Te female clown fsh are mostly dominant and large in size. Te male clown fsh are small in size. Some sub ordinates and immature clown fshes also be there. If female fsh removed or rotten, the male fshes get that position with shortest queues. Now the male becomes dominant one and the sub ordinate will become new male to acquire their social ranks in the shorter queues. Te basic performances of CFQSOA are based on four J o u r n a l o f A r t h ri t i s ISSN: 2167-7921 Journal of Arthritis Jobin Christ et al., J Arthritis 2017, 6:6 DOI: 10.4172/2167-7921.1000259 Short Communication Open Access J Arthritis, an open access journal ISSN:2167-7921 Volume 6 • Issue 6 • 1000259