MIAL

Analysis of Retinal Optical Coherence Tomography

MSc Student: Azadeh Yazdanpanah
MSc Student: Benjamin Smith
Principal Investigator: Marinko Sarunic
Principal Investigator: Ghassan Hamarneh

Optical Coherence Tomography (OCT) is a non-invasive, depth-resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We develop segmentation and analysis algorithms for OCT.

 

Overview

 

We developed a semi-automated segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapted Chan–Vese’s energy-minimizing active contours without edges for the OCT images, which suffer from low contrast and are highly corrupted by noise. A multi-phase framework with a circular shape prior is adopted in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional.

 

 

Sample Results

         
 

 

Publications

Azadeh Yazdanpanah, Ghassan Hamarneh, Ben Smith, and Marinko Sarunic. Automated Segmentation of Intra-Retinal Layers from Optical Coherence Tomography Images using an Active Contour Approach. IEEE Transactions on Medical Imaging, 30(2):484-496, 2011.  

Azadeh Yazdanpanah, Ghassan Hamarneh, Ben Smith, and Marinko Sarunic. Intra-retinal Layer Segmentation in Optical Coherence Tomography using an Active Contour Approach. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 649-656, 2009.  

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