Predictive Scheduling Algorithms for Real-Time Feature Extraction and Spatial Referencing: Application to Retinal Image Sequences

Lin, Gang; Stewart, Charles; Roysam, Badrinath; Fritzsche, Kenneth; Yang, G.‐R.; Tanenbaum, H.L.

doi:10.1109/tbme.2003.820332

Cited by 26 publications

(6 citation statements)

References 25 publications

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“…Moreover, segmentation of the vascular network has applications in retinal montage and tracking [101,102] although other approaches, not explicitly resorting to vessels can be used, as shown, for instance, by Meijering et al [103]. …”

Section: Segmentationmentioning

confidence: 99%

Digital Ocular Fundus Imaging: A Review

Bernardes

Serranho

Lobo³

2011

Ophthalmologica

174

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Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs.

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Section: Segmentationmentioning

confidence: 99%

Digital Ocular Fundus Imaging: A Review

Bernardes

Serranho

Lobo³

2011

Ophthalmologica

174

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“…Finding correspondences comprises methods using spatial relations after prematching, and methods using robust descriptors of features followed mismatch elimination. The former category used simple feature descriptor such as MI around feature points [13] or angle-based descriptor around bifurcations [26][27][28][29] to compute initial matches. Then, they used robust alignment algorithm to choose the best subset of matched Figure 1 Distribution of features for the Harris corner detector in a color image.…”

Section: Introductionmentioning

confidence: 99%

An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors

et al. 2013

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Existing algorithms based on scale invariant feature transform (SIFT) and Harris corners such as edge-driven dual-bootstrap iterative closest point and Harris-partial intensity invariant feature descriptor (PIIFD) respectivley have been shown to be robust in registering multimodal retinal images. However, they fail to register color retinal images with other modalities in the presence of large content or scale changes. Moreover, the approaches need preprocessing operations such as image resizing to do well. This restricts the application of image registration for further analysis such as change detection and image fusion. Motivated by the need for efficient registration of multimodal retinal image pairs, this paper introduces a novel integrated approach which exploits features of uniform robust scale invariant feature transform (UR-SIFT) and PIIFD. The approach is robust against low content contrast of color images and large content, appearance, and scale changes between color and other retinal image modalities like the fluorescein angiography. Due to low efficiency of standard SIFT detector for multimodal images, the UR-SIFT algorithm extracts high stable and distinctive features in the full distribution of location and scale in images. Then, feature points are adequate and repeatable. Moreover, the PIIFD descriptor is symmetric to contrast, which makes it suitable for robust multimodal image registration. After the UR-SIFT feature extraction and the PIIFD descriptor generation in images, an initial cross-matching process is performed and followed by a mismatch elimination algorithm. Our dataset consists of 120 pairs of multimodal retinal images. Experiment results show the outperformance of the UR-SIFT-PIIFD over the Harris-PIIFD and similar algorithms in terms of efficiency and positional accuracy.

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“…There have been stacks of literatures contributed to address the task. Techniques such as matching filters, piece-wise threshold probing, region growing, probabilistic approach, scale multiplication, Gaussian modeling, Gabor filters, wavelets, morphology edge operator, line operator, snake and other supervised methods have been reported in the existing work (Hoover et al, 2000;Lin et al, 2004;Lu and Lim, 2011;Narasimha-Iyer et al, 2008;Ricci and Perfetti, 2007;Sato et al, 1998;Vermeer et al, 2004;Zana and Klein, 1999). Combination of centerlines detection and morphological reconstruction also has been presented to segment the retinal vessel tree (Mendonca and Campilho, 2006).…”

Section: Introductionmentioning

confidence: 99%