The Experts below are selected from a list of 15411 Experts worldwide ranked by ideXlab platform

Fırat Hardalaç - One of the best experts on this subject based on the ideXlab platform.

  • Retinal Blood Vessel Segmentation with Neural Network by Using Gray-Level Co-Occurrence Matrix-Based Features
    Journal of Medical Systems, 2014
    Co-Authors: Javad Rahebi, Fırat Hardalaç
    Abstract:

    This paper focuses on the issue of extracting Retina vessels with supervised approach. Since the green channel in the Retina Image has the best contrast between vessel and non-vessel, this channel is used to separate vessels. In our approach we are proposing a technique of using gray-level co-occurrence matrix method for composition of the Retinal Images. It is based on fact that the co-occurrence matrix of Retina Image describes the transition of intensities between neighbour pixels, indicating spatial structural information of Retina Image. So, we first extract the features vector based on specified characteristics of the gray-level co-occurrence matrix and then we use these features vector to train a neural network approach for the classification method which makes our proposed approach more effective. Obtained results from the experiments in DRIVE and STARE database shows the advantage of the proposed method in contrast to current methods. This advantage is evaluated by the criteria of sensitivity, specificity, area under ROC and accuracy. The result of such a conversion as the input vector of a multilayer perceptron neural network will be trained and tested. Although in recent years different methods have been presented in this respect, but results of simulation shows that the proposed algorithm has a very high efficiency than the other researches.

Huajun Ying - One of the best experts on this subject based on the ideXlab platform.

  • automated localization of macula fovea area on Retina Images using blood vessel network topology
    International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Huajun Ying
    Abstract:

    In this paper, we propose a simple yet robust unsupervised algorithm for automated localization of macula-fovea area on Retina Images. The small sizes and weak contrast of the macula-fovea area on Retina Images make it unreliable to detect it directly. As such, we extract the Retina blood vessel network topology based on local energy function of blood vessel widths and densities and use it as the main Image cue to position the macula-fovea area. Regardless of the severity of most Retinal diseases as well as variations in field clarity, the high level topology of the Retinal blood vessel flows remains fairly predictable. Compared with conventional algorithms, our method can effectively localize the macula-fovea area on Retina Images with inadequate field clarity and diseased conditions. The algorithm is tested on both STARE and DRIVE Retina Image databases and gained satisfactory detection results.

Alex Pentland - One of the best experts on this subject based on the ideXlab platform.

  • screening diabetic retinopathy using an automated Retinal Image analysis system in independent and assistive use cases in mexico randomized controlled trial
    JMIR Formative Research, 2020
    Co-Authors: Alejandro Noriega, Daniela Meizner, Dalia Camacho, Jennifer Enciso, Hugo Quirozmercado, Virgilio Moralescanton, Abdullah Almaatouq, Alex Pentland
    Abstract:

    Background: The automated screening of patients at risk of developing diabetic retinopathy represents an opportunity to improve their midterm outcome and lower the public expenditure associated with direct and indirect costs of common sight-threatening complications of diabetes. Objective: This study aimed to develop and evaluate the performance of an automated deep learning–based system to classify Retinal fundus Images as referable and nonreferable diabetic retinopathy cases, from international and Mexican patients. In particular, we aimed to evaluate the performance of the automated Retina Image analysis (ARIA) system under an independent scheme (ie, only ARIA screening) and 2 assistive schemes (ie, hybrid ARIA plus ophthalmologist screening), using a web-based platform for remote Image analysis to determine and compare the sensibility and specificity of the 3 schemes. Methods: A randomized controlled experiment was performed where 17 ophthalmologists were asked to classify a series of Retinal fundus Images under 3 different conditions. The conditions were to (1) screen the fundus Image by themselves (solo); (2) screen the fundus Image after exposure to the Retina Image classification of the ARIA system (ARIA answer); and (3) screen the fundus Image after exposure to the classification of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the Image according to the ARIA system (ARIA explanation). The ophthalmologists’ classification in each condition and the result from the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of 3 Retina specialists for each fundus Image. Results: The ARIA system was able to classify referable vs nonreferable cases with an area under the receiver operating characteristic curve of 98%, a sensitivity of 95.1%, and a specificity of 91.5% for international patient cases. There was an area under the receiver operating characteristic curve of 98.3%, a sensitivity of 95.2%, and a specificity of 90% for Mexican patient cases. The ARIA system performance was more successful than the average performance of the 17 ophthalmologists enrolled in the study. Additionally, the results suggest that the ARIA system can be useful as an assistive tool, as sensitivity was significantly higher in the experimental condition where ophthalmologists were exposed to the ARIA system’s answer prior to their own classification (93.3%), compared with the sensitivity of the condition where participants assessed the Images independently (87.3%; P=.05). Conclusions: These results demonstrate that both independent and assistive use cases of the ARIA system present, for Latin American countries such as Mexico, a substantial opportunity toward expanding the monitoring capacity for the early detection of diabetes-related blindness.

  • screening diabetic retinopathy using an automated Retinal Image analysis aria system in mexico independent and assistive use cases
    medRxiv, 2020
    Co-Authors: Alejandro Noriega, Daniela Meizner, Dalia Camacho, Jennifer Enciso, Hugo Quirozmercado, Virgilio Moralescanton, Abdullah Almaatouq, Alex Pentland
    Abstract:

    Background: The automated screening of patients at risk of developing diabetic retinopathy (DR), represents an opportunity to improve their mid-term outcome and lower the public expenditure associated with direct and indirect costs of a common sight-threatening complication of diabetes. Objective: In the present study, we aim at developing and evaluating the performance of an automated deep learning-based system to classify Retinal fundus Images from international and Mexican patients, as referable and non-referable DR cases. In particular, we study the performance of the automated Retina Image analysis (ARIA) system under an independent scheme (i.e. only ARIA screening) and two assistive schemes (i.e., hybrid ARIA + ophthalmologist screening), using a web-based platform for remote Image analysis. Methods: We ran a randomized controlled experiment where 17 ophthalmologists were asked to classify a series of Retinal fundus Images under three different conditions: 1) screening the fundus Image by themselves (solo), 2) screening the fundus Image after being exposed to the opinion of the ARIA system (ARIA answer), and 3) screening the fundus Image after being exposed to the opinion of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the Image according to the ARIA system (ARIA explanation). The ophthalmologists9 opinion in each condition and the opinion of the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of three Retina specialists for each fundus Image. Results: The ARIA system was able to classify referable vs. non-referable cases with an area under the Receiver Operating Characteristic curve (AUROC), sensitivity, and specificity of 98%, 95.1% and 91.5% respectively, for international patient-cases; and an AUROC, sensitivity, and specificity of 98.3%, 95.2%, 90% respectively for Mexican patient-cases. The results achieved on Mexican patient-cases outperformed the average performance of the 17 ophthalmologist participants of the study. We also find that the ARIA system can be useful as an assistive tool, as significant specificity improvements were observed in the experimental condition where participants were exposed to the answer of the ARIA system as a second opinion (93.3%), compared to the specificity of the condition where participants assessed the Images independently (87.3%). Conclusions: These results demonstrate that both use cases of ARIA systems, independent and assistive, present a substantial opportunity for Latin American countries like Mexico towards an efficient expansion of monitoring capacity for the early detection of diabetes-related blindness.

Tan T. - One of the best experts on this subject based on the ideXlab platform.

  • Multi-modal and multi-vendor Retina Image registration
    2018
    Co-Authors: Li Z., Huang F., Zhang J., Dashtbozorg B., Abbasi-sureshjani S., Sun Y., Long X., Yu Q., Ter Haar Romeny B.m., Tan T.
    Abstract:

    Multi-modal Retinal Image registration is often required to utilize the complementary information from different Retinal imaging modalities. However, a robust and accurate registration is still a challenge due to the modality-varied resolution, contrast, and luminosity. In this paper, a two step registration method is proposed to address this problem. Descriptor matching on mean phase Images is used to globally register Images in the first step. Deformable registration based on modality independent neighbourhood descriptor (MIND) method is followed to locally refine the registration result in the second step. The proposed method is extensively evaluated on color fundus Images and scanning laser ophthalmoscope (SLO) Images. Both qualitative and quantitative tests demonstrate improved registration using the proposed method compared to the state-of-the-art. The proposed method produces significantly and substantially larger mean Dice coefficients compared to other methods (p

Tan T Tao - One of the best experts on this subject based on the ideXlab platform.

  • Multi-modal and multi-vendor Retina Image registration
    Optical Society of America (OSA), 2018
    Co-Authors: Li Zhang, Sun Y., Huang F Fan, Zhang J Jiong, Dasht Bozorg B Behdad, Abbasi-sureshjani S Samaneh, Xi X Long, Yu Qifeng, Haar Romeny, Bm Bart Ter, Tan T Tao
    Abstract:

    \u3cp\u3eMulti-modal Retinal Image registration is often required to utilize the complementary information from different Retinal imaging modalities. However, a robust and accurate registration is still a challenge due to the modality-varied resolution, contrast, and luminosity. In this paper, a two step registration method is proposed to address this problem. Descriptor matching on mean phase Images is used to globally register Images in the first step. Deformable registration based on modality independent neighbourhood descriptor (MIND) method is followed to locally refine the registration result in the second step. The proposed method is extensively evaluated on color fundus Images and scanning laser ophthalmoscope (SLO) Images. Both qualitative and quantitative tests demonstrate improved registration using the proposed method compared to the state-of-the-art. The proposed method produces significantly and substantially larger mean Dice coefficients compared to other methods (p<0.001). It may facilitate the measurement of corresponding features from different Retinal Images, which can aid in assessing certain Retinal diseases.\u3c/p\u3