Automated estimation of choroidal thickness distribution and volume based on OCT images of posterior visual section. (December 2015)

• Record Type: Journal Article
• Authors: Vupparaboina, Kiran Kumar; Nizampatnam, Srinath; Chhablani, Jay; Richhariya, Ashutosh; Jana, Soumya
• Is Part Of: Computerized medical imaging and graphics. Volume 46:Part 3(2015)
• Page Start: 315
• Page End: 327
• Publication Date: 2015-12
• Subjects: Optical coherence tomography (OCT) -- Choroidal thickness -- Choroidal volume -- Structural similarity (SSIM) index -- Tensor voting; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2015.09.008
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: Choroid is statistically separated from sclera using structural similarity index. Smoothness in choroid delineation is achieved using tensor voting. Automated choroidal volume estimation is attempted for the first time. Choroidal thickness and volume estimates are given vis-à-vis observer repeatability. Quotients are defined to fairly compare among methods tested on disparate datasets. Abstract: 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.