A pixel-based approach for classification of cardiac single photon emission computed tomography images

In this work, a computer-based algorithm is proposed for the initial interpretation of human cardiac images. Reconstructed single photon emission computed tomography images are used to differentiate between subjects with normal value and abnormal value of ejection fraction. The method analyses pixel intensities that correspond to blood flow in the left ventricular region. The algorithm proceeds through three main stages: the initial stage does a pre-processing task to reduce noise as well as blur in the image. The second stage extracts features from the images. Classification is done in the final stage. The pre-processing stage consists of a de-noising part and a de-blurring part. Novel features are used for classification. Features are extracted as three different sets based on: the pixel intensity distribution in different regions, spatial relationship of pixels and multi-scale image information. Two supervised algorithms are proposed for classification: one algorithm is based on a threshold value computed from the features extracted from the training images and the other algorithm is based on sequential minimal optimization-based support vector machine approach. Experimental studies were performed on real cardiac SPECT images obtained from hospital. The result of classification has been verified by an expert nuclear medicine physician and by the ejection fraction value obtained from quantitative gated SPECT, the most widely used software package for quantifying gated SPECT images.