Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images

Abstract

Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.

IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

“…Impediments in IVUS image segmentation are utilized to provide topological constraints on the boundary. Compared to the time-consuming extraction of features and training of classifiers [3][4][5][6][7][8], unsupervised clustering with post-assignment is fast and requires no labeled data. The iterative update of indicators D g , D a , and D s can quickly mine valuable information in the image.…”

“…Pixel classification followed by deformable models is the most common strategy for IVUS image segmentation. The classification took the morphological, textural, and grayscale features into consideration and was achieved by the extreme learning machine [3], dictionary learning in the…”

A New Fractional Particle Swarm Optimization with Entropy Diversity Based Velocity for Reactive Power Planning

“…The concept of fractional PSO is based on incorporating the fractional derivative and underlying theory inside the mathematical model of conventional PSO to improve the convergence rate while the synergy of entropy metric improve the optimization characteristic of algorithm by avoiding the suboptimal solution. In recent years, the fractional calculus based optimization mechanisms have been effectively applied in domain of science and engineering such as feature selection [ 42 ], image processing and segmentation [ 43 , 44 ], swarm robotics [ 45 ], fuzzy controllers [ 46 ], classification of extreme learning machine [ 47 ], adaptive extended Kalman filtering [ 48 ], electromagnetics [ 49 ], hyperspectral images [ 50 ], media-adventitia border detection [ 51 ], monitoring [ 52 ] and fractional adaptive filters [ 53 ]. Similarly, the inclusion of entropy diversity inside the optimizer is found to be effective in enhancing its performance by means of avoiding premature convergence [ 54 , 55 , 56 , 57 ].…”

Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

“…As a representative optimization algorithm in swarm intelligence, the Particle Swarm Optimization (PSO) algorithm has the characteristics of parallel processing and good robustness in selecting optimal parameters [28][29][30][31][32]. Besides, it can find the global optimal solution to a problem with a high probability, and its computational efficiency is higher than of the traditional stochastic algorithms.…”