The converging squares algorithm: An efficient multidimensional peak picking method

The converging squares algorithm is a method for locating peaks in sampled data of 2 dimensions or higher. There are two primary advantages of this algorithm over other conventional methods. First, it is robust with respect to noise and data type. There are no empirical parameters to allow adjustment of the process, so results arc completely objective. Secondly, the method is computationally efficient. The inherent structure of the algorithm is that of a resolution pyramid. This enhances computational efficiency as well as contributing to the quality of noise immunity of the method. The algorithm is detailed for 2-dimensional data. Quantitative comparisons of computation are made with two conventional peak picking methods.