Browsing by Author "Maduro, Cristina"
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- Adaptive complex diffusion noise despeckling for 3D OCT DataPublication . Bernardes, Rui; Maduro, Cristina; Serranho, Pedro; Dinis, João; Cunha-Vaz, JoséPurpose: To demonstrate the performance of a recently proposed despeckling filter when extended to 3D in OCT data. Methods: A new formulation for a complex diffusion filter was recently proposed [1] being adaptive in time and adjusting parameters to data, facilitating diffusion in the vitreous and reducing it in the retina to preserve tissue information. This new formulation outperformed, both quantitatively and qualitatively, currently existing filters, while at the same time was computationally more efficient attaining the same despeckling level in 34% of the computing time. We have now extended it to perform 3D OCT despeckling achieving a significant improvement in noise removal. We have resorted to a mathematical based synthetic OCT scan in order to assess quantitative results in 3D. In addition, we have applied this 3D filter to a set of 72 eye scans, from healthy volunteers (20), diabetic retinopathy (20), cystoid macular edema (2), age-related macular degeneration (20) and choroidal neo-vascularization (10), who underwent high-definition Cirrus OCT (Carl Zeiss Meditec, Dublin, CA, USA) using macular cube protocols (200x200x1024 and/or 512x128x1024). Results: The extension from 2D to 3D of this adaptive complex diffusion filter proved to be beneficial by achieving an increased level of noise reduction while simultaneously better preserving fundamental information. These facts are supported from well known metrics (e.g. MSE-mean squared error, ENL-effective number of looks and CNR-contrast-to-noise ratio) for the synthetic OCT scan and from the assessment made by 3 retina specialist who graded qualitatively the output of the 2D and 3D filters. Conclusions: These findings demonstrate this new formulation and its extension to 3D is beneficial for human analysis of OCT data and suggest it might be an important tool for automated data processing as in segmentation of retinal structures. [1] Bernardes R, Maduro C, Serranho P, Araújo A, Barbeiro S, Cunha-Vaz J. Improved adaptive complex diffusion despeckling filter. OPTICS EXPRESS 18(23):24048-24059, 2010.
- Improved adaptive complex diffusion despeckling filterPublication . Bernardes, Rui; Maduro, Cristina; Serranho, Pedro; Araújo, Adérito; Barbeiro, Sílvia; Cunha-Vaz, JoséDespeckling optical coherence tomograms from the human retina is a fundamental step to a better diagnosis or as a preprocessing stage for retinal layer segmentation. Both of these applications are particularly important in monitoring the progression of retinal disorders. In this study we propose a new formulation for a well-known nonlinear complex diffusion filter. A regularization factor is now made to be dependent on data, and the process itself is now an adaptive one. Experimental results making use of synthetic data show the good performance of the proposed formulation by achieving better quantitative results and increasing computation speed.
- Ocular fundus Imaging: from structure to functionPublication . Serranho, Pedro; Maduro, Cristina; Santos, Torcato; Bernardes, Rui; Vaz, José Cunha; Araújo, Adérito; Barbeiro, SílviaImaging the ocular fundus, namely the retina, to detect and/or monitor changes over time from the healthy condition is of fundamental importance to assess onset and disease progression and is a valuable tool to understand the basic mechanisms of ocular diseases. Current trends point to the need for less or non-invasive approaches, to the need for detailed (higher spatial and temporal resolution) imaging systems and to the quantification as opposed to qualitative classification of any findings. In this work we present a snapshot of our research by presenting two examples of technical development aiming to obtain structural and function information from the human retina, in vivo, using non-invasive techniques, namely optical coherence tomography imaging. Based on our experience and developed work, we are now starting to bridge the gap to brain imaging as the eye is only the starting point of vision.
- Synthetic volume from real optical coherence tomography dataPublication . Serranho, Pedro; Bernardes, Rui; Maduro, Cristina; Santos, Torcato; Cunha-Vaz, JoséPurpose: To build a mathematical model to mimic a real OCT b-scan/volume without noise, in order to establish a ground truth for image processing performance metrics. Methods: Current image processing techniques (eg. despeckling filtering methods) with application to optical coherence tomography (OCT) rely on the respective qualitative evaluation of its results. Quantitative approaches are reduced to using synthetic images which consists of an homogeneous background and a set of abstract objects, eg. cubes and spheres. In this work, we suggest a mathematical model to address this issue by creating a synthetic b-scan/volume based on any real OCT data scan, that can be used as ground truth for processing methods testing. Eye scans of healthy volunteers and eyes of patients with age-related macular degeneration and diabetic retinopathy were used following Cirrus OCT (Carl Zeiss Meditec, Dublin, CA, USA) scans using both the 200x200x1024 and the 512x128x1024 Macular Cube Protocols. Each of these eye scans was processed in order to extract required parameters. For healthy subjects, only the segmentation of the inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) are needed, though for pathologic eyes, the segmentation of other structures to be preserved might also be needed. In each segmented region, OCT data is surface fitted using appropriate basis functions. Results: A total of 45 scans were processed resulting in the synthetic data representing the major characteristics of the respective real OCT scans. These results have been used for testing the performance of an improved complex diffusion despeckling method proposed by some of the authors. Conclusions: This process allows to automatically compute a synthetic OCT scan mimicking a real one. In this way, this process makes it possible to quantify the result of any processing (eg. filtering) by providing adequate synthetic data as ground truth.
- Synthtic OCT Data for image processing performance testingPublication . Serranho, Pedro; Maduro, Cristina; Santos, Torcato; Vaz, José Cunha; Bernardes, RuiThe use of synthetic images is needed for testing the performance of image processing methods in order to establish a ground truth to test performance metrics. However, these synthetic images do not represent real applications. The aim of this paper is to build a mathematical model to obtain a synthetic noise-free image mimicking a real Optical Coherence Tomography (OCT) B-scan or volume from the human retina, in order to establish a ground truth for filtering performance metrics in this context. Moreover we also suggest a method to add speckle noise to this image based on the speckle noise of the given OCT volume. In this way we establish a replicable method to obtain a ground truth for image processing performance metrics that actually mimics a real case.