Automated selection of measurements for pattern recognition
Automated selection of measurements for pattern recognition
Decision rules and measurement selection in pattern recognition theory.
Decision rules and measurement selection in pattern recognition theory.
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Structured programming
Edge and Curve Detection for Visual Scene Analysis
IEEE Transactions on Computers
| Hi-index | 0.00 |
This paper describes a program for automatically extracting lung and heart features in the digitized image of posteroanterior (PA) view chest radiographs. A graph-directed analysis is used to guide the search for objects from the largest to the smallest in the radiograph. Global information is used to guide the analysis of the program. Consequently, only the points in a small range are searched and tested against local criteria to detect boundary points. The entire lung boundary is broken into four segments: upper inside boundary, lower inside boundary, boundary along the diaphragm and outside boundary. Slightly different global-local criteria for detecting the edge points along each segment have been developed and tested on 423 PA chest radiographs of patients of all ages. The results obtained indicate the program can locate the accurate boundary on all cases except infants. Twenty-seven measurements which describe the shape and size of the heart are extracted; these measurements are used for normal abnormal classification via a modified maximum likelihood classification algorithm.