Automated estimation of choroidal thickness distribution and volume based on OCT images of posterior visual section


A variety of vision ailments are indicated by anomalies in the choroid layer of the posterior visual section. Consequently, choroidal thickness and volume measurements, usually performed by experts based on optical coherence tomography (OCT) images, have assumed diagnostic significance. Now, to save precious expert time, it has become imperative to develop automated methods. To this end, one requires choroid outer boundary (COB) detection as a crucial step, where difficulty arises as the COB divides the choroidal granularity and the scleral uniformity only notionally, without marked brightness variation. In this backdrop, we measure the structural dissimilarity between choroid and sclera by structural similarity (SSIM) index, and hence estimate the COB by thresholding. Subsequently, smooth COB estimates, mimicking manual delineation, are obtained using tensor voting. On five datasets, each consisting of 97 adult OCT B-scans, automated and manual segmentation results agree visually. We also demonstrate close statistical match (greater than 99.6% correlation) between choroidal thickness distributions obtained algorithmically and manually. Further, quantitative superiority of our method is established over existing results by respective factors of 27.67% and 76.04% in two quotient measures defined relative to observer repeatability. Finally, automated choroidal volume estimation, being attempted for the first time, also yields results in close agreement with that of manual methods.

DOI: 10.1016/j.compmedimag.2015.09.008

Cite this paper

@article{Vupparaboina2015AutomatedEO, title={Automated estimation of choroidal thickness distribution and volume based on OCT images of posterior visual section}, author={Kiran Kumar Vupparaboina and Srinath Nizampatnam and Jay Chhablani and Ashutosh Richhariya and Soumya Jana}, journal={Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society}, year={2015}, volume={46 Pt 3}, pages={315-27} }