Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method

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

Compensation of Cross-Contamination in Simultaneous 201Tl/99mTc Myocardial Perfusion SPECT Imaging

Introduction: It is a common protocol to use 201Tl for the rest and 99mTc for the stress cardiac SPECT imaging. Theoretically, both types of imaging may be performed simultaneously using different energy windows for each radionuclide. However, a potential limitation is the cross-contamination of scattered photons from 99mTc and collimator X-rays into the 201Tl energy window. We used a middle energy window method to correct this cross-contamination. Material and Methods: Using NCAT, a typical software torso phantom was generated. An extremely thin line source of 99mTc activity was placed inside the cardiac region of the phantom and no activity in the other parts.  The SimSET Monte Carlo simulator was used to image the phantom in different energy windows. To find the relationship between projections in different energy windows, deconvolution theory was used. We investigated the ability of the suggested functions in three steps: Monte Carlo simulation, phantom experiment and clinical study. In the last step, SPECT images of eleven patients who had angiographic data were acquired in different energy windows. All of these images were compared by determining the contrast between a defect or left ventricle cavity and the myocardium. Results: We found a new 2D kernel which had an exponential pattern with a much higher center. This function was used for modeling 99mTc down scatter distribution from the middle window image. X-ray distribution in the 201Tl window was also modeled as the 99mTc photopeak image convolved by a Gaussian function. Significant improvements in the contrasts of the simultaneous dual 201Tl images were found in each step before and after reconstruction. In comparison with other similar methods, better results were acquired using our suggested functions. Conclusion: Our results showed contrast improvement in thallium images after correction, however, many other parameters should be evaluated for clinical approaches. There are many advantages in simultaneous dual isotope imaging. It halves imaging time and reduces patient waiting time and discomfort. Identical rest/stress registration of images also facilitates physicists’ motion or attenuation corrections and physicians’ image interpretation

Attenuation Correction in SPECT during Image Reconstruction using an Inverse Monte Carlo Method: A Simulation Study

Introduction: The main goal of SPECT imaging is to determine activity distribution inside the organs of the body. However, due to photon attenuation, it is almost impossible to do a quantitative study. In this paper, we suggest a mathematical relationship between activity distribution and its corresponding projections using a transfer matrix. Monte Carlo simulation was used to find a precise transfer matrix including the effects of photon attenuation.  Material and Methods: List mode output of the SIMIND Monte Carlo simulator was used to find the relationship between activity distribution and pixel values in projections. The MLEM iterative reconstruction method was then used to reconstruct the activity distribution from the projections. Attenuation-free projections were also simulated. Reconstructed images from these projections were used as reference images. Our suggested attenuation correction method was evaluated using three different phantom configurations: uniform activity and uniform attenuation phantom, non-uniform activity and non-uniform attenuation phantom, and NCAT torso phantom. The mean pixel values and fits between profiles were used as quantitative parameters. Results: Images free from attenuation-related artifacts were reconstructed by our suggested method. A significant increase in pixel values was found after attenuation correction. Better fits between profiles of the corrected and reference images were also found for all phantom configurations.  Discussion and Conclusion: Using a Monte Carlo method, it is possible to find the most precise relationship between activity distribution and its projections. Therefore, it is possible to create mathematical projections that include the effects of attenuation. This helps to have a more realistic comparison between mathematical and real projections, which is a necessary step for image reconstruction using MLEM. This results in images with much better quantitative accuracy at a cost of computation time and memory