? The continuing technologic improvements of CT scanners and their increasing use in screening for lung cancer has exacerbated the radiologist's task of identifying pulmonary nodules from whole lung CT scans. This has resulted in a general realization that there will be a need for computer methods to assist in this task. We have developed such a system. In this application, we seek to improve our current system and extend it to additional categories of types of nodules. We plan to use novel model-based verification methods, and to evaluate the performance of our system on our already existing large clinical database. In addition, we plan to evaluate the effect of CT scan parameters on detection performance. ? ? The detection method employs a hypothesis generation stage followed by multiple filters to eliminate false positives. It is based on cur previous methods for characterizing pulmonary nodules and involves three-dimensional image analysis techniques. We plan to extend this system to detect all types of nodules including sub-solid nodules. Furthermore, we will explore the use of model based verification methods to further reduce the number of false positives. The complete detection system will be evaluated with a CT image database that has a ground truth established by multiple radiologist reads. ? ?
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| Lee, Jaesung; Biancardi, Alberto M; Reeves, Anthony P et al. (2009) Estimation of anatomical locations using standard frame of reference in chest CT scans. Conf Proc IEEE Eng Med Biol Soc 2009:5809-12 |
| Jirapatnakul, Artit C; Fotin, Sergei V; Reeves, Anthony P et al. (2009) Automated nodule location and size estimation using a multi-scale Laplacian of Gaussian filtering approach. Conf Proc IEEE Eng Med Biol Soc 2009:1028-31 |
| Reeves, Anthony P; Chan, Antoni B; Yankelevitz, David F et al. (2006) On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 25:435-50 |
