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Ultrasound (US) is a nonionizing radiation capable of real time imaging at low cost. Its most attractive application is quantitative tissue characterization with the objective of differentiating normal tissues from diseased tissues. In this study, an automated method using singular spectrum analysis (SSA) to estimate the mean scatterer space (MSS) of US signals is proposed. Methods: Entropy was used to determine the optimal number of components for the SSA. Subsequently, this number was compared with the results using a fixed number of components. A method based on the spectrum of the original signal was also used for comparison. The method was evaluated by using 24,000 simulated US signals, i.e., echoes and jitters backscattered from samples with different ratios of regular-to-irregular structure, as well as with 152 signals obtained from a phantom made of nylon wires. Results: For the simulated signals, the proposed method for estimating the MSS presented results similar to the other methods that were tested. However, the magnitude-of-the-spectrum method loses the phase information, and hence, does not allow the characterization of irregular structures. For the signals recorded from the phantom, the methods using SSA and entropy achieved better results. Conclusion: In this study, the combination of SSA with entropy to estimate the MSS of a periodic or quasi-periodic medium was proposed. The proposed method achieved similar or better results compared with two other methods found in the scientific literature. The novelty of the proposed method is the application of entropy as a quantitative criterion for selecting the SSA periodic components, allowing it to become independent of heuristic criteria.

Mean scatterer space estimation from ultrasound signals combining singular spectral analysis and entropy