L Li1, Y Zheng, M Kallergi, R A Clark. 1. Department of Radiology, H. Lee Moffitt Cancer Research Institute, University of South Florida College of Medicine, Tampa 33612, USA.
Abstract
RATIONALE AND OBJECTIVES: The authors performed this study to evaluate an algorithm developed to help identify lungs on chest radiographs. MATERIALS AND METHODS: Forty clinical posteroanterior chest radiographs obtained in adult patients were digitized to 12-bit gray-scale resolution. In the proposed algorithm, the authors simplified the current approach of edge detection with derivatives by using only the first derivative of the horizontal and/or vertical image profiles. In addition to the derivative method, pattern classification and image feature analysis were used to determine the region of interest and lung boundaries. Instead of using the traditional curve-fitting method to delineate the lung, the authors applied an iterative contour-smoothing algorithm to each of the four detected boundary segments (costal, mediastinal, lung apex, and hemidiaphragm edges) to form a smooth lung boundary. RESULTS: The algorithm had an average accuracy of 96.0% for the right lung and 95.2% for the left lung and was especially useful in the delineation of hemidiaphragm edges. In addition, it took about 0.775 second per image to identify the lung boundaries, which is much faster than that of other algorithms noted in the literature. CONCLUSION: The computer-generated segmentation results can be used directly in the detection and compensation of rib structures and in lung nodule detection.
RATIONALE AND OBJECTIVES: The authors performed this study to evaluate an algorithm developed to help identify lungs on chest radiographs. MATERIALS AND METHODS: Forty clinical posteroanterior chest radiographs obtained in adult patients were digitized to 12-bit gray-scale resolution. In the proposed algorithm, the authors simplified the current approach of edge detection with derivatives by using only the first derivative of the horizontal and/or vertical image profiles. In addition to the derivative method, pattern classification and image feature analysis were used to determine the region of interest and lung boundaries. Instead of using the traditional curve-fitting method to delineate the lung, the authors applied an iterative contour-smoothing algorithm to each of the four detected boundary segments (costal, mediastinal, lung apex, and hemidiaphragm edges) to form a smooth lung boundary. RESULTS: The algorithm had an average accuracy of 96.0% for the right lung and 95.2% for the left lung and was especially useful in the delineation of hemidiaphragm edges. In addition, it took about 0.775 second per image to identify the lung boundaries, which is much faster than that of other algorithms noted in the literature. CONCLUSION: The computer-generated segmentation results can be used directly in the detection and compensation of rib structures and in lung nodule detection.