Automatic Detection of Hyperreflective Foci in Optical Coherence Tomography B-Scans Using Morphological Component Analysis

Abstract

Hyperreflective Foci (HF) is one of the most common complications distributed in cross-sectional images of patients with Diabetic Macular Edema (DME). Scanning Laser Ophthalmoscope (SLO) images usually consists of several B-scans that represent a cross-sectional reconstruction of a plane through the anterior or posterior regions of retina. In each B-scan, HFs are geometrically distinct constituents in different retinal layers. Since the intensity levels of HFs and many other subjects in B-scans are the same, in this paper we try to separate HFs from other objects by detection of the point and curve singularities in each B-scan. The decomposition algorithm presented in this paper is based on sparse image representation of B-scans using Morphological Component Analysis (MCA) technique. By using curvelet transform and Daubechies wavelet basis, two different over-complete dictionaries are constructed which represent two various aspects of B-scans. The HFs are more distinguished in reconstructed image with wavelet dictionary and other objects are mostly detectable by curvelet dictionary. So, HFs can be detected by applying an optimum threshold criterion on reconstructed image by wavelet atoms. Finally, the false positive points are reduced by removing the candidate points in RNFL and RPE layers, which are automatically segmented based on ridgelet transform. Our simulation results on 1924 HFs show that sensitivity and specificity for HF detection is 91.0 and 100, respectively.