Improved particle swarm optimisation to estimate bone age

This paper automatizes the process of bone age maturity assessment by applying three versions of particle swarm optimization (PSO) along with image processing methods to the left hand X-ray images. PSO versions were adopted to enhance the segmentation accuracy. The proposed method was compared to the conventional visual inspection method in terms of three segmentation criteria, classification accuracy, robustness against noise and computational complexity. Herein, PSO, worst behavior-based PSO (WB-PSO) and adaptive inertia weight (AIW-PSO) along with Otsu and an iteratively statistical method were implemented to segment the hand radiographs. A dataset containing left hand-wrist radiographs from 65 referred children was collected. Their results provided 82.49, 83.08, 84.27, 81.76 and 69.04% classification accuracy using the PSO, WB-PSO, AIW-PSO, Otsu and the iteratively statistical methods, respectively. To assess the robustness of the implemented methods, white Gaussian noise with different intensities was added to the images and the results indicated that as the noise level increased the robustness against noise for the PSO variants became more highlighted compared to the Otsu and statistical methods. Due to the convincing results, the AIW-PSO image segmentation system is suggested as an auxiliary diagnostic tool to help specialists for more accurate age bone estimation.