Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images. Issue 1 (January 2016)

• Record Type: Journal Article
• Authors: Singh, Vedpal; Elamvazuthi, Irraivan; Jeoti, Varun; George, John; Swain, Akshya; Kumar, Dileep
• Is Part Of: Biomedical engineering online. Volume 15:Issue 1(2016)
• Page Start: 1
• Page End: 20
• Publication Date: 2016-01
• Subjects: Anterior talofibular ligament -- Ultrasound imaging -- Segmentation -- Particle swarm optimization -- Adaptive histogram equalization -- Curve evolution -- Morphological operation -- Validation metrics; Biomedical engineering -- Periodicals; 610.2805
• DOI: 10.1186/s12938-016-0129-6
• Languages: English
• ISSNs: 1475-925X

Abstract

Abstract Background Anterior talofibular ligament (ATFL) is considered as the weakest ankle ligament that is most prone to injuries. Ultrasound imaging with its portable, non-invasive and non-ionizing radiation nature is increasingly being used for ATFL diagnosis. However, diagnosis of ATFL injuries requires its segmentation from ultrasound images that is a challenging task due to the existence of homogeneous intensity regions, homogeneous textures and low contrast regions in ultrasound images. To address these issues, this research has developed an efficient ATFL segmentation framework that would contribute to accurate and efficient diagnosis of ATFL injuries for clinical evaluation. Methods The developed framework comprises of five computational steps to segment the ATFL ligament region. Initially, region of interest is selected from the original image, which is followed by the adaptive histogram equalization to enhance the contrast level of the ultrasound image. The enhanced contrast image is further optimized by the particle swarm optimization algorithm. Thereafter, the optimized image is processed by the Chan–Vese method to extract the ATFL region through curve evolution; then the resultant image smoothed by morphological operation. The algorithm is tested on 25 subjects' datasets and the corresponding performance metrics are evaluated to demonstrate its clinical applicability. Results The performance of the developed framework is evaluated based on various measurement metrics. It was found that estimated computational performance of the developed framework is 12 times faster than existing Chan–Vese method. Furthermore, the developed framework yielded the average sensitivity of 98.3 %, specificity of 96.6 % and accuracy of 96.8 % as compared to the manual segmentation. In addition, the obtained distance using Hausdorff is 14.2 pixels and similarity index by Jaccard is 91 %, which are indicating the enhanced performance whilst segmented area of ATFL region obtained from five normal (average Pixels—16, 345.09), five tear (average Pixels—14, 940.96) and five thickened (average Pixels—12, 179.20) subjects' datasets show good performance of developed framework to be used in clinical practices. Conclusions On the basis of obtained results, the developed framework is computationally more efficient and more accurate with lowest rate of coefficient of variation (less than 5 %) that indicates the highest clinical significance of this research in the assessment of ATFL injuries.