Automatic Ultrasound Image Segmentation Framework Based on Darwinian Particle Swarm Optimization

Abstract

Accurate medical diagnosis and treatment necessitate the application of optimal segmentation of images. Although manual segmentation is easy, it has many problems such as time complexity and error sensitivity. On the other hand, automatic segmentation is fast with less probability of errors. However, it has many problems like low contrast image, unclear boundaries and less accu- rate. To overcome these problems, optimization methods like Particle Swarm Optimization (PSO), genetic algorithm (GA), etc. can provide more accurate and efficient outcomes. Thus, for the achievement of optimized results, the cur- rent study proposes a more optimized ‘Singh-Elamvazuthi’ ultrasound segmen- tation framework based on Darwinian Particle Swarm Optimization (D-PSO) for ankle Anterior Talofibular Ligament.