Describes a semi-automated procedure for analyzing single photon emission computed tomography (SPECT) images of a human heart and classifying the images into one of several categories: normal, infarct, ischemia, infarct and ischemia, reverse re-distribution, artifact and equivocal. The procedure aids the physician in the interpretation of SPECT images and consists of two steps. The first step processes the reconstructed SPECT images. These images contain multiple slices of 64/spl times/64 pixels with 16 bits resolution. Scanned images are converted into numerical format by using boundary extraction, region of interest (ROI) selection, and segmentation techniques. A new algorithm was developed to extract a rectangular ROI from each image. The second step involves automatic classification of the processed images into one of the seven categories, listed above, using a knowledge-based system employing machine learning algorithms (C4.5 and CLIP3) and fuzzy logic modeling, to generate classification rules. The performance of the system is measured in terms of accuracy, the gold standard being interpretation of the images by experienced cardiologists. The accuracy of the system using the rules generated by the machine learning algorithms were 94% and 81%, respectively. Accuracy, after the fuzzy linguistic variables were specified from the rules generated by the C4.5 and CLIP3 algorithms, was 91% and 86%, respectively. Overall, the system performance closely approximated that of an experienced cardiologist.
['Krzysztof J. Cios', 'Lucy S. Goodenday', 'Kanu K. Shah', 'Gursel Serpen']
A novel algorithm for classification of SPECT images of a human heart