M L Giger1, K T Bae, H MacMahon. 1. Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637.
Abstract
RATIONALE AND OBJECTIVES: Interpretation of computed tomographic (CT) scans of the lungs is a time-consuming task that involves visual correlation of possible nodules in one section with those in contiguous sections to distinguish actual nodules from blood vessels. Thus, the authors are developing automated methods to detect nodules on CT images of the thorax. METHODS: The computerized technique uses various computer-vision techniques and a priori information of the morphologic characteristics of pulmonary nodules. In each section, the external thoracic wall and lung boundaries are detected, and the features within the lung boundaries are subjected to gray-level thresholding operations. By analyzing the relationships between features arising at different threshold levels with respect to their shape, size, and location, each feature is assigned a likelihood of being a nodule or a vessel. Features in adjacent sections are compared to resolve ambiguous features. Detected nodule candidates are displayed in three dimensions within the lung. RESULTS: The system provided a sensitivity of 94% for nodule detection and an average of 1.25 false-positive results per case. CONCLUSIONS: Continued development of an automated method for detecting pulmonary nodules in CT scans is expected to aid radiologists in the task of locating nodules in three dimensions.
RATIONALE AND OBJECTIVES: Interpretation of computed tomographic (CT) scans of the lungs is a time-consuming task that involves visual correlation of possible nodules in one section with those in contiguous sections to distinguish actual nodules from blood vessels. Thus, the authors are developing automated methods to detect nodules on CT images of the thorax. METHODS: The computerized technique uses various computer-vision techniques and a priori information of the morphologic characteristics of pulmonary nodules. In each section, the external thoracic wall and lung boundaries are detected, and the features within the lung boundaries are subjected to gray-level thresholding operations. By analyzing the relationships between features arising at different threshold levels with respect to their shape, size, and location, each feature is assigned a likelihood of being a nodule or a vessel. Features in adjacent sections are compared to resolve ambiguous features. Detected nodule candidates are displayed in three dimensions within the lung. RESULTS: The system provided a sensitivity of 94% for nodule detection and an average of 1.25 false-positive results per case. CONCLUSIONS: Continued development of an automated method for detecting pulmonary nodules in CT scans is expected to aid radiologists in the task of locating nodules in three dimensions.
Authors: Leo P Lawler; Susan A Wood; Harpreet K Pannu; Elliot K Fishman; Harpreet S Pannu Journal: J Digit Imaging Date: 2003-12-15 Impact factor: 4.056