Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans.
Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists in the early detection of lung cancer in radiographs and computed tomography (CT) images. In order to improve sensitivity for nodule detection, many researchers have employed a filter as a preprocessing step for enhancement of nodules. However, these filters enhance not only nodules, but also other anatomic structures such as ribs, blood vessels, and airway walls. Therefore, nodules are often detected together with a large number of false positives caused by these normal anatomic structures. In this study, we developed three selective enhancement filters for dot, line, and plane which can simultaneously enhance objects of a specific shape (for example, dot-like nodules) and suppress objects of other shapes (for example, line-like vessels). Therefore, as preprocessing steps, these filters would be useful for improving the sensitivity of nodule detection and for reducing the number of false positives. We applied our enhancement filters to synthesized images to demonstrate that they can selectively enhance a specific shape and suppress other shapes. We also applied our enhancement filters to real two-dimensional (2D) and three-dimensional (3D) CT images to show their effectiveness in the enhancement of specific objects in real medical images. We believe that the three enhancement filters developed in this study would be useful in the computerized detection of cancer in 2D and 3D medical images.
Li
Qiang
Q
Department of Radiology, The University of Chicago, Chicago, Illinois 60637, USA. qiangli@uchicago.edu
Sone
Shusuke
S
Doi
Kunio
K
D019992:Databases as Topic; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016013:Likelihood Functions; D008168:Lung / Q000473:pathology*; D008175:Lung Neoplasms / Q000175:diagnosis*; D015233:Models, Statistical; D008962:Models, Theoretical; D013902:Radiography, Thoracic / Q000379:methods; D013909:Thorax / Q000473:pathology; D014057:Tomography, X-Ray Computed / Q000379:methods*