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A Ladder-Structured Decision Tree for Recognizing Tumors in Chest Radiographs
May 1976 (vol. 25 no. 5)
pp. 503-513
D.H. Ballard, Department of Computer Science, University of Rochester
We describe a hierarchic computer procedure for the detection of nodular tumors in a chest radiograph. The radiograph is scanned and consolidated into several resolutions which are enhanced and analyzed by a hierarchic tumor recognition process. The hierarchic structure of the tumor recognition process has the form of a ladder-like decision tree. The major steps in the decision tree are: 1) find the lung regions within the chest radiograph, 2) find candidate nodule sites (potential tumor locations) within the lung regions, 3) find boundaries for most of these sites, 4) find nodules from among the candidate nodule boundaries, and 5) find tumors from among the nodules. The first three steps locate potential nodules in the radiograph. The last two steps classify the potential nodules into nonnodules, nodules which are not tumors, and nodules which are tumors.
Index Terms:
Artificial intelligence, chest radiograph, directional edge elements, dynamic programming (DP), edge detection, heuristic problem solving, pattern recognition, picture analysis, tumor detection.
Citation:
D.H. Ballard, J. Sklansky, "A Ladder-Structured Decision Tree for Recognizing Tumors in Chest Radiographs," IEEE Transactions on Computers, vol. 25, no. 5, pp. 503-513, May 1976, doi:10.1109/TC.1976.1674638
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