Computer-Aided Lung Nodule Detection in Chest Radiography
ICSC '95 Proceedings of the Third International Computer Science Conference on Image Analysis Applications and Computer Graphics
Computer-Aided Diagnosis of Pulmonary Nodules Using Three-Dimensional Thoracic CT Images
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Detection of Pulmonary Nodules on Ct and Volumetric Assessment of Change over Time
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Expectation-Maximization for a Linear Combination of Gaussians
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Automatic analysis of 3D low dose CT images for early diagnosis of lung cancer
Pattern Recognition
Lung Nodule Modeling --- A Data-Driven Approach
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Lung nodule detection via Bayesian voxel labeling
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Appearance analysis for diagnosing malignant lung nodules
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Appearance models for robust segmentation of pulmonary nodules in 3d LDCT chest images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Automatic diagnosis of lung nodules for early detection of lung cancer is the goal of a number of screening studies worldwide. With the improvements in resolution and scanning time of low dose chest CT scanners, nodule detection and identification is continuously improving. In this paper we describe the latest improvements introduced by our group in automatic detection of lung nodules. We introduce a new template for nodule detection using level sets which describes various physical nodules irrespective of shape, size and distribution of gray levels. The template parameters are estimated automatically from the segmented data (after the first two steps of our CAD system for automatic nodule detection) – no a priori learning of the parameters’ density function is needed. We show quantitatively that this template modeling approach drastically reduces the number of false positives in the nodule detection (the third step of our CAD system for automatic nodule detection), thus improving the overall accuracy of CAD systems. We compare the performance of this approach with other approaches in the literature and with respect to human experts. The impact of the new template model includes: 1) flexibility with respect to nodule topology – thus various nodules can be detected simultaneously by the same technique; 2) automatic parameter estimation of the nodule models using the gray level information of the segmented data; and 3) the ability to provide exhaustive search for all the possible nodules in the scan without excessive processing time – this provides an enhanced accuracy of the CAD system without increase in the overall diagnosis time.