  The presence of hyperreflective foci (HFs) is related to retinal disease
progression, and the quantity has proven to be a prognostic factor of visual
and anatomical outcome in various retinal diseases. However, lack of efficient
quantitative tools for evaluating the HFs has deprived ophthalmologist of
assessing the volume of HFs. For this reason, we propose an automated
quantification algorithm to segment and quantify HFs in spectral domain optical
coherence tomography (SD-OCT). The proposed algorithm consists of two parallel
processes namely: region of interest (ROI) generation and HFs estimation. To
generate the ROI, we use morphological reconstruction to obtain the
reconstructed image and histogram constructed for data distributions and
clustering. In parallel, we estimate the HFs by extracting the extremal regions
from the connected regions obtained from a component tree. Finally, both the
ROI and the HFs estimation process are merged to obtain the segmented HFs. The
proposed algorithm was tested on 40 3D SD-OCT volumes from 40 patients
diagnosed with non-proliferative diabetic retinopathy (NPDR), proliferative
diabetic retinopathy (PDR), and diabetic macular edema (DME). The average dice
similarity coefficient (DSC) and correlation coefficient (r) are 69.70%, 0.99
for NPDR, 70.31%, 0.99 for PDR, and 71.30%, 0.99 for DME, respectively. The
proposed algorithm can provide ophthalmologist with good HFs quantitative
information, such as volume, size, and location of the HFs.
