21 March 2016 Improve synthetic retinal OCT images with present of pathologies and textural information
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Abstract
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.
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Ehsan Shahrian Varnousfaderani, Wolf-Dieter Vogl, Jing Wu, Bianca S. Gerendas, Christian Simader, Georg Langs, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, "Improve synthetic retinal OCT images with present of pathologies and textural information", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97843V (21 March 2016); doi: 10.1117/12.2217399; https://doi.org/10.1117/12.2217399
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