Parameter tuning by PSO for fuzzy inference-based coronary plaque extraction in IVUS image

In this paper, we present a method for parameter tuning of membership functions in Takagi-Sugeno (T-S) fuzzy model using Particle Swarm Optimization (PSO). This is applied to plaque boundary extraction in Intravascular Ultrasound (IVUS) image. Searching areas for coronary plaque boundaries are automatically set by using weighted image separability and some heuristic rules. The coronary plaque boundaries are interpolated by polynomials inferred by fuzzy rules. PSO tunes the parameters of the membership functions in the antecedent parts of the fuzzy rules. The accuracy of the proposed method is better than that of our previous method.