TY  - CONF
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011954453&doi=10.1109%2fICIAS.2016.7824114&partnerID=40&md5=a9aada9729849285766284a2f9f00f8d
A1  - Singh, V.
A1  - Elamvazuthi, I.
A1  - Jeoti, V.
A1  - George, J.
A1  - Kumar, D.
Y1  - 2017///
SN  - 9781509008452
PB  - Institute of Electrical and Electronics Engineers Inc.
N1  - cited By 1; Conference of 6th International Conference on Intelligent and Advanced Systems, ICIAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125970
N2  - Ultrasound image segmentation is still a challenging issue in various applications to extract the meaningful information for disease diagnosis in the athletes. Generally, ultrasound images could be suffering from some problems such as speckle, attenuation, signal dropout and shadows which make the segmentation process more complicated and inefficient. Due to these problems, traditional segmentation approaches could not be applicable. To overcome these problems, the current study proposed an automatic multilevel segmentation framework for ankle Anterior Talofibular Ligament (ATFL). This framework used the association of active contour and the particle swarm optimization method with curve evaluation and energy minimization capability to obtain the optimized segmented outcomes. It would be more efficiently detect the ATFL ligament in ultrasound images with better interpretation capability. Finally, this study presents various experimental segmented outcomes and corresponding analysis. On the basis of this analysis, the average sensitivity, specificity and accuracy of the proposed framework would be 80.73 , 96.57  and 94.12  respectively. © 2016 IEEE.
KW  - Curve fitting; Diagnosis; Image processing; Particle swarm optimization (PSO); Ultrasonic applications; Ultrasonic imaging
KW  -  Accuracy; Energy minimization; Sensitivity; Specificity; Ultrasound image segmentation
KW  -  Image segmentation
TI  - Clinical assessment of injured ankle ATFL ligaments based on ultrasound imaging in the athletes
ID  - scholars8991
AV  - none
ER  -