Automated Radiographic Diagnosis via Feature Extraction and Classification of Cardiac Size and Shape Descriptors

One goal of digital processing of radiographic images is to provide the radiologist with quantitative measurements of human anatomy as well as an indication as to whether or not this anatomy is within normal limits. A computer algorithm is described, designed to automatically detect, extract quantitative measurements from, and diagnose the cardiac projection present in full-size anteriorview chest radiographs. A normal-abnormal diagnosis is demonstrated utilizing abnormal data from five classes of heart disease. In addition, normal-abnormal as well as normal-differential diagnoses are demonstrated for the rheumatic heart disease class. A feature extraction algorithm is developed using several ad hoc techniques, some of which were adapted from other feature extraction uses. The extracted features are classified into diagnostic classes using linear and quadratic discriminant functions. A concurrent study of physician diagnostic accuracy is also undertaken using the averaged diagnostic rates of ten radiologists on a representative subset of the radiographs used in the computer study.