Automatic characterization of thoracic aortic aneurysms from CT images.

Accurate determination of the diameter is an important step for diagnosis and follow-up of aortic abnormalities such as aneurysms, caused by dilation of the vessel lumen. In this work we focus on the development of an automatic method for measuring the calibre of the thoracic aorta. The method is based on the application of principal component analysis on normal planes extracted from the aorta to establish the main axis of each section of the vessel. Two experiments were performed in order to test the accuracy and the rotational invariance of the developed method. Accuracy was determined by using a database of 15 clinical cases, where our method and a commercial software, which was considered as the gold standard, were compared. For the rotational invariance check, phantom images in different orientations were obtained and the diameter was measured with the proposed method. For clinical cases, a good agreement was observed between our method and the gold standard. The Bland Altman plots indicated that all of the values were within the acceptable limits of agreement with a bias of 0.2mm between both methods. For phantom cases, an ANOVA test revealed that the results achieved for the data sets acquired for the different orientations were not statistically different (F=1.88, p=0.153), which demonstrates the robustness of the method for rotations. The proposed method is applicable for measuring the diameter in all tested cases, and the results achieved underscored the capability of our approach for automatic characterization of thoracic aortic aneurysms.

A hybrid method based on level set and 3D region growing for segmentation of the thoracic aorta.

This study sought to develop a completely automatic method for image segmentation of the thoracic aorta. We used a total of 4682 images from 10 consecutive patients. The proposed method is based on the use of level set and region growing, automatically initialized using the Hough transform. The results obtained were compared to those of manual segmentation as performed by an external expert radiologist. Concordance between the developed method and manual segmentation ranged from 92.79 to 95.77% in the descending regions of the aorta and from 90.68 to 96.54% in the ascending regions, with a mean value of 93.83% being obtained for total segmentation.

Quantification of Right and Left Ventricular Function in Cardiac MR Imaging: Comparison of Semiautomatic and Manual Segmentation Algorithms.

The purpose of this study was to evaluate the performance of a semiautomatic segmentation method for the anatomical and functional assessment of both ventricles from cardiac cine magnetic resonance (MR) examinations, reducing user interaction to a "mouse-click". Fifty-two patients with cardiovascular diseases were examined using a 1.5-T MR imaging unit. Several parameters of both ventricles, such as end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF), were quantified by an experienced operator using the conventional method based on manually-defined contours, as the standard of reference; and a novel semiautomatic segmentation method based on edge detection, iterative thresholding and region growing techniques, for evaluation purposes. No statistically significant differences were found between the two measurement values obtained for each parameter (p > 0.05). Correlation to estimate right ventricular function was good (r > 0.8) and turned out to be excellent (r > 0.9) for the left ventricle (LV). Bland-Altman plots revealed acceptable limits of agreement between the two methods (95%). Our study findings indicate that the proposed technique allows a fast and accurate assessment of both ventricles. However, further improvements are needed to equal results achieved for the right ventricle (RV) using the conventional methodology.

Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images.

We have developed a computer-aided diagnosis (CAD) system to detect pulmonary nodules on thin-slice helical computed tomography (CT) images. We have also investigated the capability of an iris filter to discriminate between nodules and false-positive findings. Suspicious regions were characterized with features based on the iris filter output, gray level and morphological features, extracted from the CT images. Functions calculated by linear discriminant analysis (LDA) were used to reduce the number of false-positives. The system was evaluated on CT scans containing 77 pulmonary nodules. The system was trained and evaluated using two completely independent data sets. Results for a test set, evaluated with free-response receiver operating characteristic (FROC) analysis, yielded a sensitivity of 80% at 7.7 false-positives per scan.