Optical coherence tomography (OCT), being a noninvasive imaging modality, has begun to find vast use in
the diagnosis and management of ocular diseases such as glaucoma, where the retinal nerve fiber layer (RNFL)
has been known to thin. Furthermore, the recent availability of the considerably larger volumetric data with
spectral-domain OCT has increased the need for new processing techniques. In this paper, we present an
automated 3-D graph-theoretic approach for the segmentation of 7 surfaces (6 layers) of the retina from 3-D
spectral-domain OCT images centered on the optic nerve head (ONH). The multiple surfaces are detected
simultaneously through the computation of a minimum-cost closed set in a vertex-weighted graph constructed
using edge/regional information, and subject to a priori determined varying surface interaction and smoothness
constraints. The method also addresses the challenges posed by presence of the large blood vessels and the optic
disc. The algorithm was compared to the average manual tracings of two observers on a total of 15 volumetric
scans, and the border positioning error was found to be 7.25 ± 1.08 μm and 8.94 ± 3.76 μm for the normal and
glaucomatous eyes, respectively. The RNFL thickness was also computed for 26 normal and 70 glaucomatous
scans where the glaucomatous eyes showed a significant thinning (p < 0.01, mean thickness 73.7 ± 32.7 μm in
normal eyes versus 60.4 ± 25.2 μm in glaucomatous eyes).
Spinal cord (SC) tissue loss is known to occur in some patients with multiple sclerosis (MS), resulting in SC atrophy.
Currently, no measurement tools exist to determine the magnitude of SC atrophy from Magnetic Resonance Images
(MRI). We have developed and implemented a novel semi-automatic method for quantifying the cervical SC volume
(CSCV) from Magnetic Resonance Images (MRI) based on level sets. The image dataset consisted of SC MRI exams
obtained at 1.5 Tesla from 12 MS patients (10 relapsing-remitting and 2 secondary progressive) and 12 age- and gender-matched
healthy volunteers (HVs). 3D high resolution image data were acquired using an IR-FSPGR sequence acquired
in the sagittal plane. The mid-sagittal slice (MSS) was automatically located based on the entropy calculation for each of
the consecutive sagittal slices. The image data were then pre-processed by 3D anisotropic diffusion filtering for noise
reduction and edge enhancement before segmentation with a level set formulation which did not require re-initialization.
The developed method was tested against manual segmentation (considered ground truth) and intra-observer and inter-observer
variability were evaluated.
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