Objective Intravascular ultrasound (IVUS) is a diagnostic imaging technique for tomographic visualization of coronary arteries. Automatic analysis of IVUS images is difficult due to speckle noise, artifacts of the catheter, and shadows generated by calcifications. We designed and implemented a system for automated segmentation of coronary artery IVUS images. Methods Two methods for automatic detection of the intima and the media-adventitia borders in IVUS coronary artery images were developed and compared. The first method uses the parametric deformable models, while the second method is based on the geometric deformable models. The initial locations of the borders are approximated using two different edge detection methods. The final borders are then defined using the two deformable models. Finally, the calcified regions between the extracted borders are identified using a Bayesian classifier. The performance of the proposed methods was evaluated using 60 different IVUS images obtained from 7 patients. Results Segmented images were compared with manually outlined contours. We compared the performance of calcified region characterization methods using ROC analysis and

Background and Objective: Road accidents are among the major problems of transportation in Iran. There are four factors involved in road accidents, including the human, road, vehicle, and environment. Among these, human (driver) error has an important role in 70-90% of the accidents. Therefore, the present study aimed to identify and examine driver's errors using the Cognitive Reliability Error Analysis Method (CREAM). Materials and Methods:This descriptive cross-sectional study was carried out to examine a specific scenario involving driving tasks. First, driving tasks for the scenario were analyzed using the Hierarchical Task Analysis. Then, using the primary and broad CREAM techniques, possible driver controls and cognitive errors were determined for the tasks. Results: Based on the obtained results of the scenario using the primary CREAM technique, nine diver tasks were determined, including wearing a seat-belt, controlling the indicators, acceleration changing, direction changing, adjusting the distance, stopping the car, turning off the car, unbuckling the seat belt, and the light type of tactical control. Then, using the broad CREAM technique, the error levels of execution, observation, and interpretation were reported as 71.87%, 18.75%, and 9.38%, respectively. Conclusion: In the present study, four items were identified regarding the performance-reducing conditions using the primary CREAM technique. In this regard, the factor of working conditions with one case and performing two or more tasks simultaneously with three cases were introduced as the most effective performance-reducing factors that can decrease the risk of driver's errors through their reduction. Moreover, 32 driver's errors were identified according to the broad CREAM technique. Based on the results, the most common cognitive errors included execution and observation errors. With regard to the proposed controls, the risk of human errors can be reduced for the analyzed subtasks.

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Abstract. Intravascular Ultrasound (IVUS) is a diagnostic imaging techniquethat provides tomographic visualization of coronary arteries. Important challenges in analysis of IVUS images are speckle noise, artifacts of catheter and calcified shadows. In this paper, we present a method for the automated detection of outer (media-adventitia) border of vessel by the use of geometric deformable models. Speckle noise is reduced with median filter. The initial contour is extracted using Canny edge detection and finally the calcified regions are characterized by using Bayes classifier and thresholding methods. The proposed methods were evaluated on 60 IVUS images from 7 different patients. The results show that the border detection method was statistically accurate and in the range of inter observer variability (based on the used validation methods). Bayesian classifier enables us to characterize the regions of interest, with a sensitivity and specificity of 92.67% and 98.5% respectively.