Comparative evaluation of speckle reduction algorithms in optical coherence tomography

Speckle is a type of multiplicative noise degrading visual quality in imaging using optical coherence tomography (OCT) resulting in difficulty of assessment by experts. Thus speckle reduction algorithms are required for enhancing image quality of OCT and assisting in their visual assessment. The objective of this work is to evaluate the performance of speckle reduction algorithms using five image quality assessment methods. It presents a comparative evaluation of six speckle reduction filtering techniques based on local statistics, median filtering, pixel homogeneity, geometric filtering, and transformed domain homomorphic filtering. The results of this study suggest that geometric filtering algorithm outperforms other candidate methods, and exhibits minimum mean squared and Minkowski error. Also it has SNR >; 50 dB, while having best image quality and intact structural similarity. The experiment performed here for selection of a proper speckle reduction algorithm in OCT requires further large scale evaluation for application in clinical practice, exploratory analysis, automated segmentation, texture analysis, and image based classification techniques. On a wider spectrum this work is also useful as a framework for comparative assessment of similar image quality improvement algorithms.