Browsing by Author "Tavakoli, Meysam"

Now showing 1 - 7 of 7
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    Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method
    (IEEE, 2017-10) Tavakoli, Meysam; Kelley, Patrick; Nazar, Mahdieh; Kalantari, Faraz; Physics, School of Science
    The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural database (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them.
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    Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy
    (IEEE, 2021) Tavakoli, Meysam; Mehdizadeh, Alireza; Aghayan, Afshin; Shahri, Reza Pourreza; Ellis, Tim; Dehmeshki, Jamshid; Physics, School of Science
    Detection of red lesions in color retinal images is a critical step to prevent the development of vision loss and blindness associated with diabetic retinopathy (DR). Microaneurysms (MAs) are the most frequently observed and are usually the first lesions to appear as a consequence of DR. Therefore, their detection is necessary for mass screening of DR. However, detecting these lesions is a challenging task because of the low image contrast, and the wide variation of imaging conditions. Recently, the emergence of computer-aided diagnosis systems offers promising approaches to detect these lesions for diagnostic purposes. In this paper we focus on developing unsupervised and supervised techniques to cope intelligently with the MAs detection problem. In the first step, the retinal images are preprocessed to remove background variation in order to achieve a high level of accuracy in the detection. In the main processing step, important landmarks such as the optic nerve head and retinal vessels are detected and masked using the Radon transform (RT) and multi-overlapping windows. Finally, the MAs are detected and numbered by using a combination of RT and a supervised support vector machine classifier. The method was tested on three publicly available datasets and a local database comprising a total of 749 images. Detection performance was evaluated using sensitivity, specificity, and FROC analysis. From the image analysis viewpoint, DR was detected with a sensitivity of 100% and a specificity of 93% on average across all of these databases. Moreover, from lesion-based analysis the proposed approach detected the MAs with sensitivity of 95.7% with an average of 7 false positives per image. These results compare favourably with the best of the published results to date.
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    Comparison Different Vessel Segmentation Methods in Automated Microaneurysms Detection in Retinal Images using Convolutional Neural Networks
    (SPIE, 2020) Tavakoli, Meysam; Nazar, Mahdieh; Physics, School of Science
    Image processing techniques provide important assistance to physicians and relieve their workload in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by computerized approaches in a successful manner. Microaneurysms (MAs) detection is a crucial step in retinal image analysis algorithms. The goal of MAs detection is to find the progress and at last identification of diabetic retinopathy (DR) in the retinal images. The objective of this study is to apply three retinal vessel segmentation methods, Laplacian-of-Gaussian (LoG), Canny edge detector, and Matched filter to compare results of MAs detection using combination of unsupervised and supervised learning either in the normal images or in the presence of DR. The steps for the algorithm are as following: 1) Preprocessing and Enhancement, 2) vessel segmentation and masking, 3) MAs detection and Localization using combination of Matching based approach and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compared the output of our method with the ground truth that collected by ophthalmologists. By using the LoG vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs for 100 color images in a local retinal database and 40 images of a public dataset (DRIVE). For the Canny vessel segmentation, our automated algorithm found sensitivity of more than 80% in detection of MAs for all 140 images of two databases. And lastly, using Matched filter, our algorithm found sensitivity of more than 87% in detection of MAs in both local and DRIVE datasets.
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    Direct Photon-by-Photon Analysis of Time-Resolved Pulsed Excitation Data using Bayesian Nonparametrics
    (Cell Press, 2020-11-18) Tavakoli, Meysam; Jazani, Sina; Sgouralis, Ioannis; Heo, Wooseok; Ishii, Kunihiko; Tahara, Tahei; Pressé, Steve; Physics, School of Science
    Lifetimes of chemical species are typically estimated by either fitting time-correlated single-photon counting (TCSPC) histograms or phasor analysis from time-resolved photon arrivals. While both methods yield lifetimes in a computationally efficient manner, their performance is limited by choices made on the number of distinct chemical species contributing photons. However, the number of species is encoded in the photon arrival times collected for each illuminated spot and need not be set by hand a priori. Here, we propose a direct photon-by-photon analysis of data drawn from pulsed excitation experiments to infer, simultaneously and self-consistently, the number of species and their associated lifetimes from a few thousand photons. We do so by leveraging new mathematical tools within the Bayesian nonparametric. We benchmark our method for both simulated and experimental data for 1-4 species.
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    Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics
    (American Physical Society, 2020) Tavakoli, Meysam; Jazani, Sina; Sgouralis, Ioannis; Shafraz, Omer M.; Sivasankar, Sanjeevi; Donaphon, Bryan; Levitus, Marcia; Pressé, Steve; Physics, School of Science
    Fluorescence time traces are used to report on dynamical properties of molecules. The basic unit of information in these traces is the arrival time of individual photons, which carry instantaneous information from the molecule, from which they are emitted, to the detector on timescales as fast as microseconds. Thus, it is theoretically possible to monitor molecular dynamics at such timescales from traces containing only a sufficient number of photon arrivals. In practice, however, traces are stochastic and in order to deduce dynamical information through traditional means-such as fluorescence correlation spectroscopy (FCS) and related techniques-they are collected and temporally autocorrelated over several minutes. So far, it has been impossible to analyze dynamical properties of molecules on timescales approaching data acquisition without collecting long traces under the strong assumption of stationarity of the process under observation or assumptions required for the analytic derivation of a correlation function. To avoid these assumptions, we would otherwise need to estimate the instantaneous number of molecules emitting photons and their positions within the confocal volume. As the number of molecules in a typical experiment is unknown, this problem demands that we abandon the conventional analysis paradigm. Here, we exploit Bayesian nonparametrics that allow us to obtain, in a principled fashion, estimates of the same quantities as FCS but from the direct analysis of traces of photon arrivals that are significantly smaller in size, or total duration, than those required by FCS.
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    Quantitative evaluation of the effect of attenuation correction in SPECT images with CT-derived attenuation
    (SPIE, 2019) Tavakoli, Meysam; Naji, Maryam; Physics, School of Science
    In this study, we assessed the importance of attenuation correction by quantitative evaluation of errors associated with attenuation in myocardial SPECT in a phantom study. To do attenuation correction we use an attenuation map derived from X-ray CT data. The succession of attenuation correction highly depends on high quality of attenuation maps. CT derived attenuation map in related to non-uniform attenuation correction is used to do transmission dependent scatter correction. The OSEM algorithm with attenuation model was developed and used for attenuation correction during image reconstruction. Finally a comparison was done between reconstructed images using our OSEM code and analytical FBP method. The results of measurements show that: Our programs are capable to reconstruct SPECT images and correct the attenuation effects. Moreover to evaluate reconstructed image quality before and after attenuation correction we applied a famous approach using Image Quality Index. Attenuation correction increases the quality and quantity factors in both methods. This increasing is independent of activity in quantity factor and decrease with activity in quality factor. Both quantitative and qualitative of SPECT images were improved by attenuation correction. In both OSEM and FBP the activity ratio of heart phantom in comparison with the markers was very increased. So the attenuation correction in fat patients and low activity is recommended. Attenuation correction with CT images and OSEM reconstruction in the condition of complete registration yields superior results.
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    Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction
    (Wiley, 2021) Tavakoli, Meysam; Mehdizadeh, Alireza; Pourreza Shahri, Reza; Dehmeshki, Jamshid; Physics, School of Science
    Retinal blood vessel segmentation and analysis is critical for the computer-aided diagnosis of different diseases such as diabetic retinopathy. This study presents an automated unsupervised method for segmenting the retinal vasculature based on hybrid methods. The algorithm initially applies a preprocessing step using morphological operators to enhance the vessel tree structure against a non-uniform image background. The main processing applies the Radon transform to overlapping windows, followed by vessel validation, vessel refinement and vessel reconstruction to achieve the final segmentation. The method was tested on three publicly available datasets and a local database comprising a total of 188 images. Segmentation performance was evaluated using three measures: accuracy, receiver operating characteristic (ROC) analysis, and the structural similarity index. ROC analysis resulted in area under curve values of 97.39%, 97.01%, and 97.12%, for the DRIVE, STARE, and CHASE-DB1, respectively. Also, the results of accuracy were 0.9688, 0.9646, and 0.9475 for the same datasets. Finally, the average values of structural similarity index were computed for all four datasets, with average values of 0.9650 (DRIVE), 0.9641 (STARE), and 0.9625 (CHASE-DB1). These results compare with the best published results to date, exceeding their performance for several of the datasets; similar performance is found using accuracy.