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, Simplified general illustration of pan-retinal photocoagulation with a contact lens

. , The main components of the TrackScan platform developed in QuantelMedical and an illustration of SLIM during retinal examination of a patient at University Hospital of Saint-Étienne, France. (a) phantom eye; (b) contact lens; (c) binocular microscope; (d) HD sensors; (e) moving base; (f) laser supply; (g) slit-lamp; (h) live SLI sequence; (k) intra-operative retina map

, TrackScan limitations: uncorrected mosaicing drift. (a) a mismatch of treatment plan (red) with the laser spots (blue); (b) a visually distinctive vessel misalignment

, TrackScan limitations: uncorrected illumination artifacts of different degrees

. , TrackScan limitations: multi-modal registration

. , General principle of 2D image registration

, Cathedral of Clermont Ferrand. SIFT is used to describe the detected keypoints. Yellow circles visualize the position of the keypoint, the scale and orientation of the corresponding descriptor, p.17

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.. .. Spatial,

, Schematic illustration of the intensity-based image registration approach, p.23

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, Some examples of light-related artifacts in medical imaging applications, p.28

. , Some examples of light-related artifacts in SLIM

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. , 63 5.4 Number of tracks versus frames. Results show performance with UGrid key-points (red), minEig (green) and SIFT key-points (blue) respectively on the experiment without track correction, Sample images from different slit-lamp datasets. (a)-dataset#1, 253 images, (b)dataset#2, 242 images, (c)-dataset#3, 169 images, (d)-dataset#4, 309 images

, Examples of improved areas of the mosaics given in Figure 5.1 with corrected drift using proposed approach. First column-originals, second column-corrected versions, p.67

. , Typical slit-lamp images demonstrating the appearance variation of the light reflection of different origins. CWS-cotton wool spot

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. , Schematic illustration of the single-image glare removal and retina segmentation in SLIM

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. , Illustration of the inter-and intra-modality geometric and photometric variability between FA (first row) and SLIM (second raw)

.. .. Method,

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. .. , Template matching scheme for the OD and macula localization, p.85

, Example of a result of the retinal features detection in the FA reference image, p.85

. , An illustration of the training of a SOM

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. , The first column shows the normal retinal image pairs to be registered while the second column illustrates the abnormal cases within a corresponding dataset

, Performance of the automatic point matching algorithm between an AF and SLIM sample image pairs from the FA2SLIM dataset. Green crosses show the GT point correspondences while blue circles indicate the detected points using the proposed SOM-LBP method. An example with mismatched points is shown in the second row

. , From left to right the columns show the reference image with detected landmarks, the target image with detected correspondences (blue) and their GT points (green) and the registration result as a fused image, Image registration results on sample image pairs from the FA2SLIM dataset

. , Summary of the retinal mosaicing methods. n-D is a multidimensional parameter space

. , Summary of the multi-modal retinal registration methods. n-D is a multidimensional parameter space

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. , Average ? LFE and ? LCE across the different datasets

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, Tracking statistics without track correction. ?-average number of tracks per frame, ?-number of key-frames, S mean-average span, S max-maximum span, p.65

, Tracking statistics with track correction. ?-average number of tracks per frame, ?number of key-frames, S mean-average span, S max-maximum span, p.66

. , The proposed (1) is our method with UGrid used for tracks initialization and SHD based local correction step. The proposed (2) is the proposed (1) + local BA

. , Retinal content segmentation performance

. , The characteristics of the used datasets and corresponding ground truth. I O any denotes the initial number of images where the OD was either visible or not. I O vis denotes the number of images where the OD is fully visible. Subsequent Normal, Abnormal and GT values correspond to I O vis

. , Evaluation of the effect of the inclusion and exclusion of the macula detection step on the registration errors

. , Accumulated statistics over three datasets on Proportion of Correctly matched Keypoints M PCK obtained with SOM-LBP

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