Registration of bimodal retinal images - improving modifications

Kubecka, Libor; Jan, J.

doi:10.1109/iembs.2004.1403510

Cited by 26 publications

(21 citation statements)

References 13 publications

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“…It can be thought of that the more rare the occurrence of an event, the more important it is when that event does occur. Despite the wide adoption of MI, it is recognised that the method is not without limitations [12], nor can it accurately register all varieties of image modalities, and so alternative methods have since been proposed ( [18,9,20,1,4,15,21,24,27,11]). …”

Section: I(a; B) = H(a) + H(b) H(a B)mentioning

confidence: 99%

Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging

Legg

Rosin

Marshall

et al. 2015

Pattern Recognition

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Section: I(a; B) = H(a) + H(b) H(a B)mentioning

confidence: 99%

Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging

Legg

Rosin

Marshall

et al. 2015

Pattern Recognition

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“…Therefore, the more robust simulated annealing algorithm and the controlled random search (CRS) algorithm were tested. 20 As better results were achieved with the CRS algorithm, a description of this algorithm only is given in the following. CRS is a direct search technique and a pure heuristics.…”

Section: Registrationmentioning

confidence: 99%

“…See Ref. 20 for detail. An example of the result of the registration and its verification is shown in Fig.…”

Section: Registrationmentioning

confidence: 99%

Optic nerve head segmentation in multimodal retinal images

et al. 2005

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An established method for glaucoma diagnosis is the morphological analysis of the optic nerve head (ONH) by the scanning-laser-tomography (SLT). This analysis depends on prior manual outlining of the ONH. The first automated segmentation method that we developed is limited in its reliability by noise, non-uniform illumination and presence of blood vessels. Inspired by recent medical research we developed a new algorithm improving our previous method by segmenting in registered multimodal retinal images. The multimodal approach combines SLT-images with color fundus photographs (CFP). The first step of the algorithm, the registration, is based on gradient-image mutual information maximization using controlled random search as the optimization procedure. The kernel of the segmentation module consists in the anchored active contours. The initial contour is obtained from the CFP. The points the initial curve should be attracted to, the anchors, are constrained by the Hough transform applied to a morphologically processed SLT-image. The false anchors are eliminated by masking out blood vessels that are extracted in the CFP. The method was tested on 174 multimodal image pairs. The overall performance of the system yielded 89% correctly segmented ONH, qualitatively evaluated comparing the automated contours with manual ones drawn by an experienced ophthalmologist. This represents an appreciable improvement in reliability (from 74% to 89%) compared to monomodal approach. The developed method is the basis for a promising tool for glaucoma screening.

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“…Color images are 3,888 × 768 pixels, and SLO images are 768 × 768 pixels. The registration of SLO and color images would help an analysis of the optic disc in the early detection of glaucoma [12].…”

Section: Input Image Pairsmentioning

confidence: 99%

“…Most common similarity measures in the context of multimodal RIR are mutual information (MI) [11,12], entropy correlation coefficient (ECC) [13], and phase correlation [14]. However, MI performance degrades when faced with a large amount of changes in the texture of retinal image and changes in scale [15].…”

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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Registration of bimodal retinal images - improving modifications

Cited by 26 publications

References 13 publications

Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging

Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging

Optic nerve head segmentation in multimodal retinal images

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

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