A Geodesic Active Contour Level Set Method for Image Segmentation

Arthritis is a joint disorder featuring inflammation. There are numerous forms of Arthritis. Arthritis essentially causes joint dis-functioning which may further tend to cause deformity and disability. Osteoarthritis (OA) is one form of arthritis which is mostly seen in old age group. A patient suffering from OA needs to visit medical experts where clinical and radiographic examination is carried out. Analysis of bone structures in initial stage is bit complex. So any vague conclusion drawn from the radiographic images may make the treatment faulty and troublesome. Thus to overcome this we have developed an algorithm that computes the cartilage area/thickness using various shape descriptors. The computed descriptors obtained the accuracy of 99.81% for K-nearest neighbour classifier and 95.09% for decision tree classifier. The estimated cartilage thickness is validated by radiographic experts as per KL grading framework which will be helpful to the doctors for quick and appropriate analysis of ailment in the early stage. The results are competitive and promising as reported in the literature.

Section: Active Contour Segmentationmentioning

confidence: 99%

Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images

Gornale¹,

Patravali²

2019

“…This function consists of internal, external and shape energy [21,22]. Active contour representations have been applied in different fields to track non-rigid objects [22,23,24]. An active contour representation is defined as in (1).…”

Section: B Contour Detectionmentioning

confidence: 99%

An Automatic Video-based Drowning Detection System for Swimming Pools Using Active Contours

Salehi¹,

Keyvanara²,

Monadjemmi³

2016

Safety in swimming pools is a crucial issue. In this paper, a real time drowning detection method based on HSV color space analysis is presented which uses prior knowledge of the video sequences to set the best values for the color channels. Our method uses a HSV thresholding mechanism along with Contour detection to detect the region of interest in each frame of video sequences. The presented software can detect drowning person in indoor swimming pools and sends an alarm to the lifeguard rescues if the previously detected person is missing for a specific amount of time. The presented algorithm for this system is tested on several video sequences recorded in swimming pools in real conditions and the results are of high accuracy with a high capability of tracking individuals in real time. According to the evaluation results, the number of false alarms generated by the system is minimal and the maximum alarm delay reported by the system is 2.6 sec which can relatively be reliable compared to the acceptable time for rescue and resuscitation.

“…The active contour process can be normally divided into two categories, the explicit active contours presented by point sets and the implicit active contours presented by level set functions [22]. In this work, by using implicit active contour, the region of interest for a given knee xray is segmented by considering specific 12x12 window (mask), which act as an input to active contour model of Chan-Vese method.…”

Section: Image Segmentationmentioning

confidence: 99%

“…However, there is still a need to carefully examine the parameters that are concerned with OA. Further, several authors [22][23][24][25] have employed morphological processing of images and edge feature extraction and classification using active contours, Contourlets, HOG, SVM methods to obtain more accurate results suitable for different applications. In this paper, the objective is to build an appropriate and robust image processing algorithm for monitoring the OA ailment in early stage and evaluating according to the standard KL grading framework.…”

Section: Related Workmentioning

confidence: 99%

Determination of Osteoarthritis Using Histogram of Oriented Gradients and Multiclass SVM

Gornale¹,

Patravali²,

Marathe³

et al. 2017

Abstract-Knee Osteoarthritis is most ordinary kind of joint inflammation, which often occurs in one or both the knee joints. Osteoarthritis is additionally called as 'wear and tear' process of joint that results in dynamic disintegration of articular cartilage. Cartilage is smooth substantial layer that ensures movement to occur effortlessly. In Osteoarthritis, the cartilage is inclined towards the destruction as it loses elasticity and becomes brittle.Osteoarthritis is regularly investigated from radiographic evaluation after clinical examination. In any case, a visual evaluation made by the restorative physician depends on experience that varies subjectively and is profoundly reliant on their experience. Subsequently, in order to make diagnostic process more systematic and reliable, evolution of imaging based analysis for early recognition of Osteoarthritis is required. The objective of this study is to develop a machine vision approach for investigation of Knee Osteoarthritis using region based and active shape model. The computation involves histogram of oriented gradient (HOG) method. The processed HOG elements are computed using multiclass SVM for evaluating Osteoarthritis based on Kellgren and Lawrence (KL) grading system. The classification rate of 97.96% for Grade-0, 92.85% for Grade-1, 86.20% for Grade-2, 100% for Grade-3 & Grade-4 is obtained. The results are promising and competitive which are validated by the medical experts.