Application of Independent Component Analysis Techniques in Speckle Noise Reduction of Retinal OCT Images

TL;DR

This paper explores the novel application of Independent Component Analysis (ICA) techniques for reducing speckle noise in retinal OCT images, demonstrating potential benefits especially with fewer B-scans.

Contribution

It provides the first comparative analysis of ICA methods (InfoMax, JADE, FastICA, SOBI) for OCT speckle noise reduction, including a new noise reduction pipeline.

Findings

ICA improves noise reduction in OCT images.

Fewer B-scans benefit more from ICA techniques.

ICA methods vary in computational complexity.

Abstract

Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Therefore the need for speckle noise reduction techniques is of high importance. To the best of our knowledge, use of Independent Component Analysis (ICA) techniques has never been explored for speckle reduction of OCT images. Here, a comparative study of several ICA techniques (InfoMax, JADE, FastICA and SOBI) is provided for noise reduction of retinal OCT images. Having multiple B-scans of the same location, the eye movements are compensated using a rigid registration technique. Then, different ICA techniques are applied to the aggregated set of B-scans for extracting the noise-free image. Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Equivalent-Number-of-Looks (ENL), as well as analysis on the computational complexity of the methods, are considered as metrics for comparison. The results show that use of ICA can be beneficial, especially in case of having fewer number of B-scans.