3D vs. 2D cephalometric analysis comparisons with repeated measurements from 20 Thai males and 20 Thai females

This paper presented 3D cephalometric analysis on DICOM data from I-CAT CT cone-beam machine consisted of averages and standard deviations from 20 Thai males from 19 to 70 year (average 33.53 ± 14.08 year) and 20 Thai females from 16 to 70 year (average 32.60 ± 15.37 year). The angular measurements consisted of 49 lateral angular measurements and 9 frontal angular measurements while linear measurements consisted of 29 lateral linear measurements, 3 frontal linear measurements, and 8 perpendicular measurements. Results in 3D were compared with the corresponding 2D results showing that most midline-to-midline linear measurements and some midline-to-midline angular measurements were not different, while other types of measurements were significantly different. The 3D results will be used in the clinical Ceph3D services as requested by those with interests on cephalometric analysis and anthropology with focus on Thai subjects while the 2D results will be used for comparison with cephalometric analyses from other orthodontists.ts reserved

Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images

Diabetic Retinopathy (DR) is a severe and widely spread eye disease. Exudates are one of the most prevalent signs during the early stage of DR and an early detection of these lesions is vital to prevent the patient’s blindness. Hence, detection of exudates is an important diagnostic task of DR, in which computer assistance may play a major role. In this paper, a system based on local feature extraction and Support Vector Machine (SVM) classification is used to develop and compare different strategies for automated detection of exudates. The main novelty of this work is allowing the detection of exudates using non-regular regions to perform the local feature extraction. To accomplish this objective, different methods for generating superpixels are applied to the fundus images of E-OPHTA database and texture and morphological features are extracted for each of the resulting regions. An exhaustive comparison among the proposed methods is also carried out.This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.Pereira, J.; Colomer, A.; Naranjo Ornedo, V. (2018). Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 174-183. https://doi.org/10.1007/978-3-030-03493-1_19S174183Sidibé, D., Sadek, I., Mériaudeau, F.: Discrimination of retinal images containing bright lesions using sparse coded features and SVM. Comput. Biol. 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Eng. 51(2), 246–254 (2004)Welfer, D., Scharcanski, J., Marinho, D.R.: A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Comput. Med. Imaging Graph. 34(3), 228–235 (2010)Giancardo, L., et al.: Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med. Image Anal. 16(1), 216–226 (2012)Amel, F., Mohammed, M., Abdelhafid, B.: Improvement of the hard exudates detection method used for computer-aided diagnosis of diabetic retinopathy. Int. J. Image Graph. Signal Process. 4(4), 19 (2012)Akram, M.U., Khalid, S., Tariq, A., Khan, S.A., Azam, F.: Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput. Biol. Med. 45, 161–171 (2014)Akram, M.U., Tariq, A., Khan, S.A., Javed, M.Y.: Automated detection of exudates and macula for grading of diabetic macular edema. Comput. Methods Programs Biomed. 114(2), 141–152 (2014)Machairas, V.: Waterpixels and their application to image segmentation learning. Ph.D. thesis, Université de recherche Paris Sciences et Lettres (2016)Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 211–224. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15555-0_16Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.: TurboPixels: fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. 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Optic Disc and Fovea Localisation in Ultra-widefield Scanning Laser Ophthalmoscope Images Captured in Multiple Modalities

We propose a convolutional neural network for localising the centres of the optic disc (OD) and fovea in ultra-wide field of view scanning laser ophthalmoscope (UWFoV-SLO) images of the retina. Images captured in both reflectance and autofluorescence (AF) modes, and central pole and eyesteered gazes, were used. The method achieved an OD localisation accuracy of 99.4% within one OD radius, and fovea localisation accuracy of 99.1% within one OD radius on a test set comprising of 1790 images. The performance of fovea localisation in AF images was comparable to the variation between human annotators at this task. The laterality of the image (whether the image is of the left or right eye) was inferred from the OD and fovea coordinates with an accuracy of 99.9%

Fovea and Optic Disc Detection in Retinal Images with Visible Lesions

Part 19: Workshop on Data Analysis and Modeling Retina in Health and DiseaseInternational audienceThe work herein presented aims at improving the detection of the fovea and optic disk anatomical structures in color fundus photographs from patients with visible lesions. The detection algorithm consists of five steps: selection of an area in the image where the optic disk is located using Sobel operator, extraction of optic disk boundaries applying the Hough transform to detect center and diameter of optic disk, detection of the ROI (region of interest) where the fovea is located based on the optic disk center and its diameter, detection of the fovea within the ROI. The proposed improvement adds an additional mask based on an estimation of the vessels present so that lesions that lie away from the vessels do not influence the Sobel operator result. The developed algorithm has been tested in public datasets such as STARE and also on proprietary dataset with 1464 images (ground truth generated by experts)

Automated Localization and Accurate Segmentation of Optic Disc Based on Intensity within a Minimum Enclosing Circle

Part 6: Decision AlgorithmsInternational audienceThis paper presents a method for automated localization and accurate segmentation of the optic disc. An intensity threshold is determined and select all the pixels whose intensities are greater than the threshold, by erosion the optic disc can be localized. By dilation and region filling, a minimum enclosing circle which can completely hold the optic disc is determined, within the circle, the vessels are eliminated by replacing the darker vessel pixels with brighter pixels. Define the intensity features of the optic disc boundary , and select the pixels according to the features, then the optic disc may be segmented. The experiment shows that compared to the active contour models, this method is more efficient and accurate on the boundary extraction of the optic disc