Automatic classification of 123I-FP-CIT (DaTSCAN) SPECT images

Introduction We present a method of automatic classification of I-123-fluoropropyl-carbomethoxy-3 beta-4-iodophenyltropane (FP-CIT) images. This technique uses singular value decomposition (SVD) to reduce a training set of patient image data into vectors in feature space (D space). The automatic classification techniques use the distribution of the training data in D space to define classification boundaries. Subsequent patients can be mapped into D space, and their classification can be automatically given. Methods The technique has been tested using 116 patients for whom the diagnosis of either Parkinsonian syndrome or non-Parkinsonian syndrome has been confirmed from post I-123-FP-CIT imaging follow-up. The first three components were used to define D space. Two automatic classification tools were used, naive Bayes (NB) and group prototype. A leave-one-out cross-validation was performed to repeatedly train and test the automatic classification system. Four commercially available systems for the classification were tested using the same clinical database. Results The proposed technique combining SVD and NB correctly classified 110 of 116 patients (94.8%), with a sensitivity of 93.7% and specificity of 97.3%. The combination of SVD and an automatic classifier performed as well or better than the commercially available systems. Conclusion The combination of data reduction by SVD with automatic classifiers such as NB can provide good diagnostic accuracy and may be a useful adjunct to clinical reporting. Nucl Med Commun 32:699-707 (C) 2011 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins.