The Experts below are selected from a list of 23709 Experts worldwide ranked by ideXlab platform
Michael D Abramoff - One of the best experts on this subject based on the ideXlab platform.
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End-to-End Adversarial Retinal Image Synthesis
IEEE Transactions on Medical Imaging, 2017Co-Authors: Pedro Costa, Adrian Galdran, Maria Ines Meyer, Michael D Abramoff, Meindert Niemeijer, Ana Maria Mendonca, Aurelio CampilhoAbstract:In medical Image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing Retinal color Images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of Retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color Retinal Images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end Retinal Image synthesis system capable of generating as many Retinal Images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of Retinal Images, e.g., smoothly interpolating new data points between two Retinal Images. Visual and quantitative results demonstrate that the synthesized Images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.
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towards adversarial Retinal Image synthesis
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Pedro Costa, Adrian Galdran, Maria Ines Meyer, Michael D Abramoff, Meindert Niemeijer, Ana Maria Mendonca, Aurelio CampilhoAbstract:Synthesizing Images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New Images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus Images directly from data. For that, we pair true eye fundus Images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new Retinal Image. For this purpose, we use a recent Image-to-Image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated Images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic Retinal Images confirms that the produced Images retain a high proportion of the true Image set quality.
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objective and expert independent validation of Retinal Image registration algorithms by a projective imaging distortion model
Medical Image Analysis, 2010Co-Authors: Sangyeol Lee, Michael D Abramoff, Joseph M Reinhardt, Philippe C CattinAbstract:Fundus camera imaging of the retina is widely used to diagnose and manage ophthalmologic disorders including diabetic retinopathy, glaucoma, and age-related macular degeneration. Retinal Images typically have a limited field of view, and multiple Images can be joined together using an Image registration technique to form a montage with a larger field of view. A variety of methods for Retinal Image registration have been proposed, but evaluating such methods objectively is difficult due to the lack of a reference standard for the true alignment of the individual Images that make up the montage. A method of generating simulated Retinal Images by modeling the geometric distortions due to the eye geometry and the Image acquisition process is described in this paper. We also present a validation process that can be used for any Retinal Image registration method by tracing through the distortion path and assessing the geometric misalignment in the coordinate system of the reference standard. The proposed method can be used to perform an accuracy evaluation over the whole Image, so that distortion in the non-overlapping regions of the montage components can be easily assessed. We demonstrate the technique by generating test Image sets with a variety of overlap conditions and compare the accuracy of several Retinal Image registration models.
Aurelio Campilho - One of the best experts on this subject based on the ideXlab platform.
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eyequal accurate explainable Retinal Image quality assessment
International Conference on Machine Learning and Applications, 2017Co-Authors: Pedro Costa, Aurelio Campilho, Bryan Hooi, Asim Smailagic, Kris M Kitani, Sheng Hua Liu, Christos Faloutsos, Adrian GaldranAbstract:Given a Retinal Image, can we automatically determine whether it is of high quality (suitable for medical diagnosis)? Can we also explain our decision, pinpointing the region or regions that led to our decision? Images from human retinas are vital for the diagnosis of multiple health issues, like hypertension, diabetes, and Alzheimer’s; low quality Images may force the patient to come back again for a second scanning, wasting time and possibly delaying treatment. However, existing Retinal Image quality assessment methods are either black boxes without explanations of the results or depend heavily on feature engineering or on complex and error-prone anatomical structures’ segmentation. Therefore, we propose EyeQual, that solves exactly this problem. EyeQual is novel, fast for inference, accurate and explainable, pinpointing low-quality regions on the Image. We evaluated EyeQual on two real datasets where it achieved 100% accuracy taking just 36 milliseconds for each Image.
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End-to-End Adversarial Retinal Image Synthesis
IEEE Transactions on Medical Imaging, 2017Co-Authors: Pedro Costa, Adrian Galdran, Maria Ines Meyer, Michael D Abramoff, Meindert Niemeijer, Ana Maria Mendonca, Aurelio CampilhoAbstract:In medical Image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing Retinal color Images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of Retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color Retinal Images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end Retinal Image synthesis system capable of generating as many Retinal Images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of Retinal Images, e.g., smoothly interpolating new data points between two Retinal Images. Visual and quantitative results demonstrate that the synthesized Images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.
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towards adversarial Retinal Image synthesis
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Pedro Costa, Adrian Galdran, Maria Ines Meyer, Michael D Abramoff, Meindert Niemeijer, Ana Maria Mendonca, Aurelio CampilhoAbstract:Synthesizing Images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New Images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus Images directly from data. For that, we pair true eye fundus Images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new Retinal Image. For this purpose, we use a recent Image-to-Image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated Images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic Retinal Images confirms that the produced Images retain a high proportion of the true Image set quality.
Charles V Stewart - One of the best experts on this subject based on the ideXlab platform.
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computer vision algorithms for Retinal Image analysis current results and future directions
International Conference on Computer Vision, 2005Co-Authors: Charles V StewartAbstract:Automated Image analysis tools have the potential to play an important role in assisting in the diagnosis and treatment of Retinal diseases. Problems that must be addressed in developing these tools include extraction of vascular and non-vascular features, segmentation of pathologies, unimodal and multimodal Image registration, mosaic construction, and real-time systems. Research at Rensselaer Polytechnic Institute since the late 1990’s has focused on several of these problems. Most significantly, we have developed a series of registration and mosaic formation algorithms which have been validated on thousands of Retinal Images and have been extended beyond the retina application. While the core fundus Image registration problem is essentially solved, important problems remain in many aspects of Retinal Image analysis.
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the dual bootstrap iterative closest point algorithm with application to Retinal Image registration
IEEE Transactions on Medical Imaging, 2003Co-Authors: Charles V Stewart, Chialing Tsai, Badrinath RoysamAbstract:Motivated by the problem of Retinal Image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small Image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between Images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering Retinal Image pairs, Dual-Bootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns Images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 Image pairs, it successfully registered 99.5% of the pairs containing at least one common landmark, and 100% of the pairs containing at least one common landmark and at least 35% Image overlap.
Pedro Costa - One of the best experts on this subject based on the ideXlab platform.
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eyequal accurate explainable Retinal Image quality assessment
International Conference on Machine Learning and Applications, 2017Co-Authors: Pedro Costa, Aurelio Campilho, Bryan Hooi, Asim Smailagic, Kris M Kitani, Sheng Hua Liu, Christos Faloutsos, Adrian GaldranAbstract:Given a Retinal Image, can we automatically determine whether it is of high quality (suitable for medical diagnosis)? Can we also explain our decision, pinpointing the region or regions that led to our decision? Images from human retinas are vital for the diagnosis of multiple health issues, like hypertension, diabetes, and Alzheimer’s; low quality Images may force the patient to come back again for a second scanning, wasting time and possibly delaying treatment. However, existing Retinal Image quality assessment methods are either black boxes without explanations of the results or depend heavily on feature engineering or on complex and error-prone anatomical structures’ segmentation. Therefore, we propose EyeQual, that solves exactly this problem. EyeQual is novel, fast for inference, accurate and explainable, pinpointing low-quality regions on the Image. We evaluated EyeQual on two real datasets where it achieved 100% accuracy taking just 36 milliseconds for each Image.
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End-to-End Adversarial Retinal Image Synthesis
IEEE Transactions on Medical Imaging, 2017Co-Authors: Pedro Costa, Adrian Galdran, Maria Ines Meyer, Michael D Abramoff, Meindert Niemeijer, Ana Maria Mendonca, Aurelio CampilhoAbstract:In medical Image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing Retinal color Images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of Retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color Retinal Images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end Retinal Image synthesis system capable of generating as many Retinal Images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of Retinal Images, e.g., smoothly interpolating new data points between two Retinal Images. Visual and quantitative results demonstrate that the synthesized Images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.
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towards adversarial Retinal Image synthesis
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Pedro Costa, Adrian Galdran, Maria Ines Meyer, Michael D Abramoff, Meindert Niemeijer, Ana Maria Mendonca, Aurelio CampilhoAbstract:Synthesizing Images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New Images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus Images directly from data. For that, we pair true eye fundus Images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new Retinal Image. For this purpose, we use a recent Image-to-Image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated Images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic Retinal Images confirms that the produced Images retain a high proportion of the true Image set quality.
Jian Zheng - One of the best experts on this subject based on the ideXlab platform.
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Salient Feature Region: A New Method for Retinal Image Registration
IEEE Transactions on Information Technology in Biomedicine, 2011Co-Authors: Jian Zheng, Jie Tian, Kexin Deng, Xing Zhang, Min XuAbstract:Retinal Image registration is crucial for the diagnoses and treatments of various eye diseases. A great number of methods have been developed to solve this problem; however, fast and accurate registration of low-quality Retinal Images is still a challenging problem since the low content contrast, large intensity variance as well as deterioration of unhealthy retina caused by various pathologies. This paper provides a new Retinal Image registration method based on salient feature region (SFR). We first propose a well-defined region saliency measure that consists of both local adaptive variance and gradient field entropy to extract the SFRs in each Image. Next, an innovative local feature descriptor that combines gradient field distribution with corresponding geometric information is then computed to match the SFRs accurately. After that, normalized cross-correlation-based local rigid registration is performed on those matched SFRs to refine the accuracy of local alignment. Finally, the two Images are registered by adopting high-order global transformation model with locally well-aligned region centers as control points. Experimental results show that our method is quite effective for Retinal Image registration.
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A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Jian Chen, Jian Zheng, Jie Tian, Theodore R. Smith, Andrew F. LaineAbstract:Detection of vascular bifurcations is a challenging task in multimodal Retinal Image registration. Existing algorithms based on bifurcations usually fail in correctly aligning poor quality Retinal Image pairs. To solve this problem, we propose a novel highly distinctive local feature descriptor named partial intensity invariant feature descriptor (PIIFD) and describe a robust automatic Retinal Image registration framework named Harris-PIIFD. PIIFD is invariant to Image rotation, partially invariant to Image intensity, affine transformation, and viewpoint/perspective change. Our Harris-PIIFD framework consists of four steps. First, corner points are used as control point candidates instead of bifurcations since corner points are sufficient and uniformly distributed across the Image domain. Second, PIIFDs are extracted for all corner points, and a bilateral matching technique is applied to identify corresponding PIIFDs matches between Image pairs. Third, incorrect matches are removed and inaccurate matches are refined. Finally, an adaptive transformation is used to register the Image pairs. PIIFD is so distinctive that it can be correctly identified even in nonvascular areas. When tested on 168 pairs of multimodal Retinal Images, the Harris-PIIFD far outperforms existing algorithms in terms of robustness, accuracy, and computational efficiency.