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Robert J Zawadzki - One of the best experts on this subject based on the ideXlab platform.

  • polarization properties of Retinal Blood Vessel walls measured with polarization sensitive optical coherence tomography
    Biomedical Optics Express, 2021
    Co-Authors: Hadi Afsharan, Michael J Hackmann, Qiang Wang, Farzaneh Navaeipour, Stephy Vijaya Kumar Jayasree, Robert J Zawadzki, Dilusha Silva, Chulmin Joo, Barry Cense
    Abstract:

    A new method based on polarization-sensitive optical coherence tomography (PS-OCT) is introduced to determine the polarization properties of human Retinal Vessel walls, in vivo. Measurements were obtained near the optic nerve head of three healthy human subjects. The double pass phase retardation per unit depth (DPPR/UD), which is proportional to the birefringence, is higher in artery walls, presumably because of the presence of muscle tissue. Measurements in surrounding Retinal nerve fiber layer tissue yielded lower DPPR/UD values, suggesting that the Retinal Vessel wall tissue near the optic nerve is not covered by Retinal nerve fiber layer tissue (0.43°/µm vs. 0.77°/µm, respectively). Measurements were obtained from multiple artery-vein pairs, to quantify the different polarization properties. Measurements were taken along a section of the Vessel wall, with changes in DPPR/UD up to 15%, while the Vessel wall thickness remained relatively constant. A stationary scan pattern was applied to determine the influence of involuntary eye motion on the measurement, which was significant. Measurements were also analyzed by two examiners, with high inter-observer agreement. The measurement repeatability was determined with measurements that were acquired during multiple visits. An improvement in accuracy can be achieved with an ultra-broad-bandwidth PS-OCT system since it will provide more data points in-depth, which reduces the influence of discretization and helps to facilitate better fitting of the birefringence data.

  • an efficient Retinal Blood Vessel segmentation in eye fundus images by using optimized top hat and homomorphic filtering
    Computer Methods and Programs in Biomedicine, 2021
    Co-Authors: Oscar Ramossoto, Aboul Ella Hassanien, Erick Rodriguezesparza, Sandra E Balderasmata, Diego Oliva, Ratheesh Kumar Meleppat, Robert J Zawadzki
    Abstract:

    Abstract Background and objective: Automatic segmentation of Retinal Blood Vessels makes a major contribution in CADx of various ophthalmic and cardiovascular diseases. A procedure to segment thin and thick Retinal Vessels is essential for medical analysis and diagnosis of related diseases. In this article, a novel methodology for robust Vessel segmentation is proposed, handling the existing challenges presented in the literature. Methods: The proposed methodology consists of three stages, pre-processing, main processing, and post-processing. The first stage consists of applying filters for image smoothing. The main processing stage is divided into two configurations, the first to segment thick Vessels through the new optimized top-hat, homomorphic filtering, and median filter. Then, the second configuration is used to segment thin Vessels using the proposed optimized top-hat, homomorphic filtering, matched filter, and segmentation using the MCET-HHO multilevel algorithm. Finally, morphological image operations are carried out in the post-processing stage. Results: The proposed approach was assessed by using two publicly available databases (DRIVE and STARE) through three performance metrics: specificity, sensitivity, and accuracy. Analyzing the obtained results, an average of 0.9860, 0.7578 and 0.9667 were respectively achieved for DRIVE dataset and 0.9836, 0.7474 and 0.9580 for STARE dataset. Conclusions: The numerical results obtained by the proposed technique, achieve competitive average values with the up-to-date techniques. The proposed approach outperform all leading unsupervised methods discussed in terms of specificity and accuracy. In addition, it outperforms most of the state-of-the-art supervised methods without the computational cost associated with these algorithms. Detailed visual analysis has shown that a more precise segmentation of thin Vessels was possible with the proposed approach when compared with other procedures.

Aboul Ella Hassanien - One of the best experts on this subject based on the ideXlab platform.

  • an efficient Retinal Blood Vessel segmentation in eye fundus images by using optimized top hat and homomorphic filtering
    Computer Methods and Programs in Biomedicine, 2021
    Co-Authors: Oscar Ramossoto, Aboul Ella Hassanien, Erick Rodriguezesparza, Sandra E Balderasmata, Diego Oliva, Ratheesh Kumar Meleppat, Robert J Zawadzki
    Abstract:

    Abstract Background and objective: Automatic segmentation of Retinal Blood Vessels makes a major contribution in CADx of various ophthalmic and cardiovascular diseases. A procedure to segment thin and thick Retinal Vessels is essential for medical analysis and diagnosis of related diseases. In this article, a novel methodology for robust Vessel segmentation is proposed, handling the existing challenges presented in the literature. Methods: The proposed methodology consists of three stages, pre-processing, main processing, and post-processing. The first stage consists of applying filters for image smoothing. The main processing stage is divided into two configurations, the first to segment thick Vessels through the new optimized top-hat, homomorphic filtering, and median filter. Then, the second configuration is used to segment thin Vessels using the proposed optimized top-hat, homomorphic filtering, matched filter, and segmentation using the MCET-HHO multilevel algorithm. Finally, morphological image operations are carried out in the post-processing stage. Results: The proposed approach was assessed by using two publicly available databases (DRIVE and STARE) through three performance metrics: specificity, sensitivity, and accuracy. Analyzing the obtained results, an average of 0.9860, 0.7578 and 0.9667 were respectively achieved for DRIVE dataset and 0.9836, 0.7474 and 0.9580 for STARE dataset. Conclusions: The numerical results obtained by the proposed technique, achieve competitive average values with the up-to-date techniques. The proposed approach outperform all leading unsupervised methods discussed in terms of specificity and accuracy. In addition, it outperforms most of the state-of-the-art supervised methods without the computational cost associated with these algorithms. Detailed visual analysis has shown that a more precise segmentation of thin Vessels was possible with the proposed approach when compared with other procedures.

  • Retinal Blood Vessel localization approach based on bee colony swarm optimization fuzzy c means and pattern search
    Journal of Visual Communication and Image Representation, 2015
    Co-Authors: Aboul Ella Hassanien, Eid Emary, Hossam M Zawbaa
    Abstract:

    The proposed Vessel segmentation based on bee colony swarm optimization and fuzzy c-means is comprised of the following three fundamental building phases: Pre-processing phase: In the first phase of the investigation, was adapted to enhance the main brightness of the retina images before the actual segmentation is performed. Segmentation phase: In the second phase, bee colony swarm optimization with fuzzy cluster compactness fitness is applied to find clusters and further the obtained clusters are refined using pattern search with thinness fitness function. Post-processing phase: is applied for improving the segmentation accuracy by removing the non-thin connected components and gap filling. These three phases are described in detail in the following section along with the steps involved and the characteristics feature for each phase and the overall architecture of the introduced approach is described in the following figure.Display Omitted An automated Retinal Blood Vessels segmentation approach based on two levels optimization principles is proposed.Uses the artificial bee colony optimization in conjunction with fuzzy cluster compactness fitness function.Pattern search is further used to enhance the segmentation results using shape description as a complementary feature. Accurate segmentation of Retinal Blood Vessels is an important task in computer aided diagnosis and surgery planning of retinopathy. Despite the high resolution of photographs in fundus photography, the contrast between the Blood Vessels and Retinal background tends to be poor. Furthermore, pathological changes of the Retinal Vessel tree can be observed in a variety of diseases such as diabetes and glaucoma. Vessels with small diameters are much liable to effects of diseases and imaging problems. In this paper, an automated Retinal Blood Vessels segmentation approach based on two levels optimization principles is proposed. The proposed approach makes use of the artificial bee colony optimization in conjunction with fuzzy cluster compactness fitness function with partial belongness in the first level to find coarse Vessels. The dependency on the Vessel reflectance is problematic as the confusion with background and Vessel distortions especially for thin Vessels, so we made use of a second level of optimization. In the second level of optimization, pattern search is further used to enhance the segmentation results using shape description as a complementary feature. Thinness ratio is used as a fitness function for the pattern search optimization. The pattern search is a powerful tool for local search while artificial bee colony is a global search with high convergence speed. The proposed Retinal Blood Vessels segmentation approach is tested on two publicly available databases DRIVE and STARE of Retinal images. The results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of sensitivity, specificity and accuracy.

  • Retinal Blood Vessel segmentation approach based on mathematical morphology
    Procedia Computer Science, 2015
    Co-Authors: Gehad Hassan, Nashwa Elbendary, Aboul Ella Hassanien, Ali Fahmy, Shoeb M Abullah, Vaclav Snasel
    Abstract:

    Abstract Diabetic retinopathy is a disease, which forms a severe threat on sight. It may reach to blindness among working age people. By analyzing and detecting of vasculature structures in Retinal images, we can early detect the diabetes in advanced stages by comparison of its states of Retinal Blood Vessels. In this paper, we present Blood Vessel segmentation approach, which can be used in computer based Retinal image analysis to extract the Retinal image Vessels. Mathematical morphology and K-means clustering are used to segment the Vessels. To enhance the Blood Vessels and suppress the background information, we perform smoothing operation on the Retinal image using mathematical morphology. Then the enhanced image is segmented using K-means clustering algorithm. The proposed approach is tested on the DRIVE dataset and is compared with alternative approaches. Experimental results obtained by the proposed approach showed that it is effective as it achieved average accuracy of 95.10% and best accuracy of 96.25%.

  • Retinal Blood Vessel segmentation using bee colony optimisation and pattern search
    International Joint Conference on Neural Network, 2014
    Co-Authors: Eid Emary, Aboul Ella Hassanien, Hossam M Zawbaa, Gerald Schaefer, Ahmad Taher Azar
    Abstract:

    Accurate segmentation of Retinal Blood Vessels is an important task in computer aided diagnosis of retinopathy. In this paper, we propose an automated Retinal Blood Vessel segmentation approach based on artificial bee colony optimisation in conjunction with fuzzy c-means clustering. Artificial bee colony optimisation is applied as a global search method to find cluster centers of the fuzzy c-means objective function. Vessels with small diameters appear distorted and hence cannot be correctly segmented at the first segmentation level due to confusion with nearby pixels. We employ a pattern search approach to optimisation in order to localise small Vessels with a different fitness function. The proposed algorithm is tested on the publicly available DRIVE and STARE Retinal image databases and confirmed to deliver performance that is comparable with state-of-the-art techniques in terms of accuracy, sensitivity and specificity.

Aurelio Campilho - One of the best experts on this subject based on the ideXlab platform.

  • epistemic and heteroscedastic uncertainty estimation in Retinal Blood Vessel segmentation
    U.Porto Journal of Engineering, 2021
    Co-Authors: Pedro Alves Costa, Asim Smailagic, Jaime S Cardoso, Aurelio Campilho
    Abstract:

    Current state-of-the-art medical image segmentation methods require high quality datasets to obtain good performance. However, medical specialists often disagree on diagnosis, hence, datasets contain contradictory annotations. This, in turn, leads to difficulties in the optimization process of Deep Learning models and hinder performance. We propose a method to estimate uncertainty in Convolutional Neural Network (CNN) segmentation models, that makes the training of CNNs more robust to contradictory annotations. In this work, we model two types of uncertainty, heteroscedastic and epistemic, without adding any additional supervisory signal other than the ground-truth segmentation mask. As expected, the uncertainty is higher closer to Vessel boundaries, and on top of thinner and less visible Vessels where it is more likely for medical specialists to disagree. Therefore, our method is more suitable to learn from datasets created with heterogeneous annotators. We show that there is a correlation between the uncertainty estimated by our method and the disagreement in the segmentation provided by two different medical specialists. Furthermore, by explicitly modeling the uncertainty, the Intersection over Union of the segmentation network improves 5.7 percentage points.

  • parametric model fitting based approach for Retinal Blood Vessel caliber estimation in eye fundus images
    PLOS ONE, 2018
    Co-Authors: Teresa Araujo, Aurelio Campilho, Ana Maria Mendonca
    Abstract:

    Background Changes in the Retinal Vessel caliber are associated with a variety of major diseases, namely diabetes, hypertension and atherosclerosis. The clinical assessment of these changes in fundus images is tiresome and prone to errors and thus automatic methods are desirable for objective and precise caliber measurement. However, the variability of Blood Vessel appearance, image quality and resolution make the development of these tools a non-trivial task. Metholodogy A method for the estimation of Vessel caliber in eye fundus images via Vessel cross-sectional intensity profile model fitting is herein proposed. First, the Vessel centerlines are determined and individual segments are extracted and smoothed by spline approximation. Then, the corresponding cross-sectional intensity profiles are determined, post-processed and ultimately fitted by newly proposed parametric models. These models are based on Difference-of-Gaussians (DoG) curves modified through a multiplying line with varying inclination. With this, the proposed models can describe profile asymmetry, allowing a good adjustment to the most difficult profiles, namely those showing central light reflex. Finally, the parameters of the best-fit model are used to determine the Vessel width using ensembles of bagged regression trees with random feature selection. Results and conclusions The performance of our approach is evaluated on the REVIEW public dataset by comparing the Vessel cross-sectional profile fitting of the proposed modified DoG models with 7 and 8 parameters against a Hermite model with 6 parameters. Results on different goodness of fitness metrics indicate that our models are constantly better at fitting the Vessel profiles. Furthermore, our width measurement algorithm achieves a precision close to the observers, outperforming state-of-the art methods, and retrieving the highest precision when evaluated using cross-validation. This high performance supports the robustness of the algorithm and validates its use in Retinal Vessel width measurement and possible integration in a system for Retinal vasculature assessment.

Arif Ahmed Sekh - One of the best experts on this subject based on the ideXlab platform.

  • Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis
    Journal of Medical Systems, 2020
    Co-Authors: Debasis Maji, Arif Ahmed Sekh
    Abstract:

    Automatic grading of Retinal Blood Vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of Retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of Blood Vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on Vessel width, tortuosity, and Vessel branching are also used for grading. However, the automated method that directly can come to a decision by taking the fundus images got less attention. Detecting eye problems based on the tortuosity of the Vessel from fundus images is a complicated task for opthalmologists. So automated grading algorithm using deep learning can be most valuable for grading Retinal health. The aim of this work is to develop an automatic computer aided diagnosis system to solve the problem. This work approaches to achieve an automatic grading method that is opted using Convolutional Neural Network (CNN) model. In this work we have studied the state-of-the-art machine learning algorithms and proposed an attention network which can grade Retinal images. The proposed method is validated on a public dataset EIARG1, which is only publicly available dataset for such task as per our knowledge.

Muhammad Moazam Fraz - One of the best experts on this subject based on the ideXlab platform.

  • optimizing the trainable b cosfire filter for Retinal Blood Vessel segmentation
    PeerJ, 2018
    Co-Authors: Sufian A Badawi, Muhammad Moazam Fraz
    Abstract:

    Segmentation of the Retinal Blood Vessels using filtering techniques is a widely used step in the development of an automated system for diagnostic Retinal image analysis. This paper optimized the Blood Vessel segmentation, by extending the trainable B-COSFIRE filter via identification of more optimal parameters. The filter parameters are introduced using an optimization procedure to three public datasets (STARE, DRIVE, and CHASE-DB1). The suggested approach considers analyzing thresholding parameters selection followed by application of background artifacts removal techniques. The approach results are better than the other state of the art methods used for Vessel segmentation. ANOVA analysis technique is also used to identify the most significant parameters that are impacting the performance results (p-value i 0.05). The proposed enhancement has improved the Vessel segmentation accuracy in DRIVE, STARE and CHASE-DB1 to 95.47, 95.30 and 95.30, respectively.

  • application of morphological bit planes in Retinal Blood Vessel extraction
    Journal of Digital Imaging, 2013
    Co-Authors: Muhammad Moazam Fraz, Abdul Basit, Sarah Barman
    Abstract:

    The appearance of the Retinal Blood Vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal Blood Vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of Blood Vessels in Retinal images. A unique combination of methods for Retinal Blood Vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the Blood Vessels from the color Retinal images. The skeleton of main Vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the Retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the Vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of Vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented Vessel tree.

  • an ensemble classification based approach applied to Retinal Blood Vessel segmentation
    IEEE Transactions on Biomedical Engineering, 2012
    Co-Authors: Muhammad Moazam Fraz, Paolo Remagnino, Andreas Hoppe, Bunyarit Uyyanonvara, Alicja R Rudnicka, Christopher G Owen, Sarah Barman
    Abstract:

    This paper presents a new supervised method for segmentation of Blood Vessels in Retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological Retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public Retinal Vessel reference dataset CHASE_DB1 which is a subset of Retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated Retinal image analysis.