Model-based cardiac diagnosis of pulmonary embolism
Computer Methods and Programs in Biomedicine
Methodology for automatic detection of lung nodules in computerized tomography images
Computer Methods and Programs in Biomedicine
Unique parameter identification for cardiac diagnosis in critical care using minimal data sets
Computer Methods and Programs in Biomedicine
Segmentation of ultrasound images of the carotid using RANSAC and cubic splines
Computer Methods and Programs in Biomedicine
Patient specific identification of the cardiac driver function in a cardiovascular system model
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering
Computer Methods and Programs in Biomedicine
Aneurysm identification by analysis of the blood-vessel skeleton
Computer Methods and Programs in Biomedicine
Validation of subject-specific cardiovascular system models from porcine measurements
Computer Methods and Programs in Biomedicine
A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces
Computer Methods and Programs in Biomedicine
Computer-aided diagnosis system: A Bayesian hybrid classification method
Computer Methods and Programs in Biomedicine
Lung cancer classification using neural networks for CT images
Computer Methods and Programs in Biomedicine
Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor
Computer Methods and Programs in Biomedicine
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In this paper, we propose a new computer-aided detection (CAD) - based method to detect pulmonary embolism (PE) in computed tomography angiography images (CTAI). Since lung vessel segmentation is the main objective to provide high sensitivity in PE detection, this method performs accurate lung vessel segmentation. To concatenate clogged vessels due to PEs, the starting region of PEs and some reference points (RPs) are determined. These RPs are detected according to the fixed anatomical structures. After lung vessel tree is segmented, the region, intensity, and size of PEs are used to distinguish them. We used the data sets that have heart disease or abnormal tissues because of lung disease except PE in this work. According to the results, 428 of 450 PEs, labeled by the radiologists from 33 patients, have been detected. The sensitivity of the developed system is 95.1% at 14.4 false positive per data set (FP/ds). With this performance, the proposed CAD system is found quite useful to use as a second reader by the radiologists.