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Philip N Cascade

Publications and source records attributed to Philip N Cascade.

8 recordsLinked to original sources
PubMed2006

Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.

We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface, (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (A(z)) of 0.83 +/- 0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC model for best classification were generally larger than those outlined by the LIDC radiologists using visual judgment of nodule boundaries.

Biopsy↗
PubMed2006

CT and MRI of coronary artery disease: evidence-based review.

OBJECTIVE: The educational objective of this evidence-based self-assessment module is to use case examples to review the current evidence and the roles of CT and MRI in evaluating and managing patients with both congenital and acquired coronary artery disease. CONCLUSION: In this educational module, we review the use of CT and MRI in the noninvasive diagnosis and management of patients with coronary artery disease.

Adult↗
PubMed2005

Preliminary investigation of computer-aided detection of pulmonary embolism in three-dimensional computed tomography pulmonary angiography images.

RATIONALE AND OBJECTIVES: We sought to develop a computer-aided diagnosis (CAD) system for assisting radiologists in the detection of pulmonary embolism (PE) on computed tomography pulmonary angiographic (CTPA) images. MATERIALS AND METHODS: An adaptive three-dimensional (3D) voxel clustering method was developed based on expectation-maximization (EM) analysis to extract vessels from their surrounding tissues. Using a connected component analysis, the vessel tree was reconstructed by tracking the vessel and its branches in 3D space. The tracked vessels were prescreened for suspicious PE areas using a second EM analysis. A rule-based false-positive (FP) reduction method was designed to detect true PE based on the features of PE and vessels. In this preliminary study, 14 patients with positive CTPA for PE were studied. CT scans were performed at 1.25-mm collimation using a GE LightSpeed CT scanner; eight of these patients also had extensive lung parenchymal or pleural disease. One hundred sixty-three emboli were identified by two experienced thoracic radiologists. The emboli identified by the radiologists were used as the "gold standard." For each embolus, the percent diameter occlusion (clot) and conspicuity of embolus (rating of 1 to 5, with 5 being the most conspicuous) were visually estimated. One hundred one emboli were identified in the six patients without lung diseases; 57 were proximal to the subsegmental and 44 were subsegmental. For the eight patients with lung diseases, 62 emboli were identified, of which 37 were proximal to the subsegmental and 25 were subsegmental. A computer-detected volume was counted as true-positive when it overlapped with an embolus volume identified by the radiologists. RESULTS: In the cases without lung diseases, if the PE had a conspicuity of >2 and only partially (20%-80%) occluded the vessel, our method detected 92.0% of proximal emboli and 77.8% of subsegmental emboli, with an average of 18.3 FPs/case. In the cases containing extensive lung disease, 66.7% and 40.0% of the PEs were detected with an average of 11.4 FPs/case under the same conditions. For the 14 PE cases, 13 of them were diagnosed as positive PE cases (case sensitivity was 92.9%). CONCLUSION: This preliminary study indicates that our automated method is a promising approach to CAD of PE on CTPA. Further study is under way to collect a larger data set and to improve the detection accuracy for PE, especially those with <20% or >80% occlusion, and for very subtle PE. A fully developed CAD system is expected to provide a useful aid for PE detection on CTPA.

Angiography↗
PubMed2005

Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting.

We are developing a computer-aided detection system to assist radiologists in the detection of lung nodules on thoracic computed tomography (CT) images. The purpose of this study was to improve the false-positive (FP) reduction stage of our algorithm by developing features that extract three-dimensional (3D) shape information from volumes of interest identified in the prescreening stage. We formulated 3D gradient field descriptors, and derived 19 gradient field features from their statistics. Six ellipsoid features were obtained by computing the lengths and the length ratios of the principal axes of an ellipsoid fitted to a segmented object. Both the gradient field features and the ellipsoid features were designed to distinguish spherical objects such as lung nodules from elongated objects such as vessels. The FP reduction performance in this new 25-dimensional feature space was compared to the performance in a 19-dimensional space that consisted of features extracted using previously developed methods. The performance in the 44-dimensional combined feature space was also evaluated. Linear discriminant analysis with stepwise feature selection was used for classification. The parameters used for feature selection were optimized using the simplex algorithm. Training and testing were performed using a leave-one-patient-out scheme. The FP reduction performances in different feature spaces were evaluated by using the area Az under the receiver operating characteristic curve and the number of FPs per CT section at a given sensitivity as accuracy measures. Our data set consisted of 82 CT scans (3551 axial sections) from 56 patients with section thickness ranging from 1.0 to 2.5 mm. Our prescreening algorithm detected 111 of the 116 solid nodules (nodule size: 3.0-30.6 mm) marked by experienced thoracic radiologists. The test Az values were 0.95 +/- 0.01, 0.88 +/- 0.02, and 0.94 +/- 0.01 in the new, previous, and combined feature spaces, respectively. The number of FPs per section at 80% sensitivity in these three feature spaces were 0.37, 1.61, and 0.34, respectively. The improvement in the test Az with the 25 new features was statistically significant (p<0.0001) compared to that with the previous 19 features alone.

Algorithms↗
PubMed2003

Pulmonary embolism: optimization of small pulmonary artery visualization at multi-detector row CT.

PURPOSE: To compare the frequency of well-visualized pulmonary arteries according to anatomic level by using different collimation with single- and multi-detector row computed tomography (CT) in patients suspected of having acute pulmonary embolism. MATERIALS AND METHODS: Sixty patients were examined with one of three techniques (20 patients each). Group 1 was examined with single-detector row CT with 3-mm collimation and 1.3-1.6 pitch; groups 2 and 3, with multi-detector row CT with 2.5- and 1.25-mm collimation, respectively. Three thoracic radiologists independently reviewed examination findings to determine if each main, lobar, segmental, and subsegmental artery was well visualized for presence of pulmonary embolism. chi2 tests were performed. For well-visualized vessels, the presence and/or absence of pulmonary embolism was recorded and kappa statistic was determined. RESULTS: Reader 1 scored 95% (114 of 120), 96% (115 of 120), and 99% (119 of 120) of lobar arteries (P >.05); 76% (304 of 400), 86% (346 of 400), and 91% (363 of 400) of segmental arteries (P <.001); and 37% (300 of 800), 56% (448 of 800), and 76% (608 of 800) of subsegmental arteries as well visualized (P <.001) using techniques 1, 2, and 3, respectively. Reader 2 scored 97% (116 of 120), 95% (114 of 120), and 99% (119 of 120) of lobar arteries (P >.05); 77% (308 of 400), 87% (349 of 400), and 93% (371 of 400) of segmental arteries (P <.001); and 39% (310 of 800), 53% (422 of 800), and 78% (621 of 800) of subsegmental arteries (P <.001) as well visualized using techniques 1, 2, and 3, respectively. Reader 3 scored 86% (103 of 120), 82% (98 of 120), and 91% (109 of 120) of lobar arteries (P >.05); 63% (252 of 400), 70% (280 of 400), and 85% (339 of 400) of segmental arteries (P <.001); and 39% (310 of 800), 56% (451 of 800), and 71% (572 of 800) of subsegmental arteries (P <.001) as well visualized using techniques 1, 2, and 3, respectively. Sixteen patients had pulmonary embolism. Interobserver agreement for detection of pulmonary embolism was significantly better for segmental and subsegmental arteries for all readers with technique 3 (segmental, kappa = 0.79-0.80; subsegmental, kappa = 0.71-0.76) than that with technique 1 (segmental, kappa = 0.47-0.75; subsegmental, kappa = 0.28-0.54). CONCLUSION: Multi-detector row CT at 1.25-mm collimation significantly improves visualization of segmental and subsegmental arteries and interobserver agreement in detection of pulmonary embolism.

Adult↗
PubMed2002

Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system.

We are developing a computer-aided diagnosis (CAD) system for lung nodule detection on thoracic helical computed tomography (CT) images. In the first stage of this CAD system, lung regions are identified by a k-means clustering technique. Each lung slice is classified as belonging to the upper, middle, or the lower part of the lung volume. Within each lung region, structures are segmented again using weighted k-means clustering. These structures may include true lung nodules and normal structures consisting mainly of blood vessels. Rule-based classifiers are designed to distinguish nodules and normal structures using 2D and 3D features. After rule-based classification, linear discriminant analysis (LDA) is used to further reduce the number of false positive (FP) objects. We performed a preliminary study using 1454 CT slices from 34 patients with 63 lung nodules. When only LDA classification was applied to the segmented objects, the sensitivity was 84% (53/63) with 5.48 (7961/1454) FP objects per slice. When rule-based classification was used before LDA, the free response receiver operating characteristic (FROC) curve improved over the entire sensitivity and specificity ranges of interest. In particular, the FP rate decreased to 1.74 (2530/1454) objects per slice at the same sensitivity. Thus, compared to FP reduction with LDA alone, the inclusion of rule-based classification lead to an improvement in detection accuracy for the CAD system. These preliminary results demonstrate the feasibility of our approach to lung nodule detection and FP reduction on CT images.

Adolescent↗
PubMedDate not supplied

Cardiovascular MR imaging: technique optimization and detection of disease in clinical practice.

Magnetic resonance (MR) imaging has emerged as an important and growing means of cardiovascular imaging, with many advantages over other radiologic modalities, including excellent spatial and temporal resolution, lack of ionizing radiation, and noninvasiveness. In this article, the utility of MR imaging in cardiovascular imaging and in the diagnosis of cardiovascular disease will be discussed. MR techniques for evaluating the heart and vasculature will be described, and troubleshooting techniques will be presented. Imaging findings in congenital anomalies such as septal defects, patent ductus arteriosus, transposition of the great arteries, and tetralogy of Fallot will be identified. Valvular lesions and methods for evaluating valvular function will be discussed. MR imaging findings in acquired disorders such as aneurysms and pericardial disease will be described.

Cardiovascular Diseases↗