Improved Method for Automatic Identification of Lung Regions on Chest Radiographs
Section snippets
Data Set
Forty adult posteroanterior chest radiographs were used in this study. All radiographs were obtained from the research database at the University of South Florida Medical School, Tampa, and digitized by using a scanner (Lumiscan-75; Lumisys, Tucson, Ariz) with a pixel size of 100 μm. The digital chest images had 12-bit gray-scale resolution. For better viewing and faster processing, each input image was first subsampled to one-fourth of the original size (average size, 2,000 × 2,400). Although
Results
We performed a pixel-by-pixel analysis to qualify the performance of the algorithm (6). Each pixel of an image was placed into one of four categories: true-positive, where both the algorithm and the human observer considered the pixel to be within the lung; true-negative, where both considered the pixel to be outside the lung; false-positive, where the computer found the pixel inside the lung and the observer did not; and false-negative, where the observer detected the pixel in the lung but the
Discussion
First derivatives are usually considered unreliable in edge detection because they are based on the local feature of the image and are sensitive to noise. We successfully used first derivatives, however, after making some modifications on the basis of image feature analysis and pattern classification. In this algorithm, we first reduced the irrelevant information (including the noise) contained in the image by identifying the ROI (the lung region). Then, we used first derivatives of the image
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