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The lack of noise free Optical Coherence Tomography (OCT) images makes it challenging to quantitatively evaluate performance of image processing methods such as denoising methods. The synthetic noise free OCT images are needed to evaluate performance of image processing methods. The current synthetic methods fail to generate synthetic images that represent real OCT images with present of pathologies. They cannot correctly imitate real OCT data due to a tendency to smooth the data, losing texture information and even, pathologies such as cysts are simply smoothed away by these methods. The first aim of this paper is to use mathematical models to generate a synthetic noise free image that represent real retinal OCT B-scan or volume with present of clinically important pathologies. The proposed method partitions a B-scan obtained from real OCT into three regions (vitreous, retina and choroid) by segmenting the inner limiting membrane (ILM) and retinal pigment epithelium (RPE) surfaces as well as cysts regions by medical experts. Then retina region is further divided into small blocks. Different smoothness functions are used to estimate OCT signals in vitreous, choroid and cyst regions and in blocks of retina region. Estimating signals in block resolution enables our proposed method to capture more textural information by using a simple mathematical model (smoothness function) and using annotated cyst enables our method to model cyst pathology accurately. The qualitative evaluations show that proposed method generates more realistic B-scans with present of pathologies and textural information than other methods.

Optical coherence tomography (OCT) is an optical signal acquisition method capturing micrometer resolution, cross-sectional three-dimensional images. OCT images are used widely in ophthalmology to diagnose and monitor retinal diseases such as age-related macular degeneration (AMD) and Glaucoma. While OCT allows the visualization of retinal structures such as vessels and retinal layers, image quality and contrast is reduced by speckle noise, obfuscating small, low intensity structures and structural boundaries. Existing denoising methods for OCT images may remove clinically significant image features such as texture and boundaries of anomalies. In this paper, we propose a novel patch based denoising method, Geodesic Denoising. The method reduces noise in OCT images while preserving clinically significant, although small, pathological structures, such as fluid-filled cysts in diseased retinas. Our method selects optimal image patch distribution representations based on geodesic patch similarity to noisy samples. Patch distributions are then randomly sampled to build a set of best matching candidates for every noisy sample, and the denoised value is computed based on a geodesic weighted average of the best candidate samples. Our method is evaluated qualitatively on real pathological OCT scans and quantitatively on a proposed set of ground truth, noise free synthetic OCT scans with artificially added noise and pathologies. Experimental results show that performance of our method is comparable with state of the art denoising methods while outperforming them in preserving the critical clinically relevant structures.

Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high- resolution, three-dimensional (3D) cross-sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD), glaucoma and retinal vein occlusion (RVO). Disease diagnosis, assessment, and treatment will require a patient to undergo multiple OCT scans, possibly using multiple scanners, to accurately and precisely gauge disease activity, progression and treatment success. However, cross-vendor imaging and patient movement may result in poor scan spatial correlation potentially leading to incorrect diagnosis or treatment analysis. The retinal fovea is the location of the highest visual acuity and is present in all patients, thus it is critical to vision and highly suitable for use as a primary landmark for cross-vendor/cross-patient registration for precise comparison of disease states. However, the location of the fovea in diseased eyes is extremely challenging to locate due to varying appearance and the presence of retinal layer destroying pathology. Thus categorising and detecting the fovea type is an important prior stage to automatically computing the fovea position. Presented here is an automated cross-vendor method for fovea distinction in 3D SD-OCT scans of patients suffering from RVO, categorising scans into three distinct types. OCT scans are preprocessed by motion correction and noise filing followed by segmentation using a kernel graph-cut approach. A statistically derived mask is applied to the resulting scan creating an ROI around the probable fovea location from which the uppermost retinal surface is delineated. For a normal appearance retina, minimisation to zero thickness is computed using the top two retinal surfaces. 3D local minima detection and layer thickness analysis are used to differentiate between the remaining two fovea types. Validation employs ground truth fovea types identified by clinical experts at the Vienna Reading Center (VRC). The results presented here are intended to show the feasibility of this method for the accurate and reproducible distinction of retinal fovea types from multiple vendor 3D SD-OCT scans of patients suffering from RVO, and for use in fovea position detection systems as a landmark for intra- and cross-vendor 3D OCT registration.

Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high reso- lution, three-dimensional (3D) cross sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD) and glaucoma.1 Disease diagnosis, assessment, and treatment requires a patient to undergo multiple OCT scans, possibly using different scanning devices, to accurately and precisely gauge disease activity, progression and treatment success. However, the use of OCT imaging devices from different vendors, combined with patient movement may result in poor scan spatial correlation, potentially leading to incorrect patient diagnosis or treatment analysis. Image registration can be used to precisely compare disease states by registering differing 3D scans to one another. In order to align 3D scans from different time- points and vendors using registration, landmarks are required, the most obvious being the retinal vasculature. Presented here is a fully automated cross-vendor method to acquire retina vessel locations for OCT registration from fovea centred 3D SD-OCT scans based on vessel shadows. Noise filtered OCT scans are flattened based on vendor retinal layer segmentation, to extract the retinal pigment epithelium (RPE) layer of the retina. Voxel based layer profile analysis and k-means clustering is used to extract candidate vessel shadow regions from the RPE layer. In conjunction, the extracted RPE layers are combined to generate a projection image featuring all candidate vessel shadows. Image processing methods for vessel segmentation of the OCT constructed projection image are then applied to optimize the accuracy of OCT vessel shadow segmentation through the removal of false positive shadow regions such as those caused by exudates and cysts. Validation of segmented vessel shadows uses ground truth vessel shadow regions identified by expert graders at the Vienna Reading Center (VRC). The results presented here are intended to show the feasibility of this method for the accurate and precise extraction of suitable retinal vessel shadows from multiple vendor 3D SD-OCT scans for use in intra-vendor and cross-vendor 3D OCT registration, 2D fundus registration and actual retinal vessel segmentation. The resulting percentage of true vessel shadow segments to false positive segments identified by the proposed system compared to mean grader ground truth is 95%.