The ability of four methods to perform automatic texture discrimination of three cellular organelles (nucleus, mitochondria and lipid droplets) from autoradiographic images is investigated. The four methods studied are the first-order statistics of the gray-level histogram, the gray-level difference method, the gray-level run length method, and the spatial gray-level dependence method. The influence of parameters like the number of features, the number of gray-level classes, the orientation and step size of the analysis, and the effect of preprocessing the images by histogram equalization and image reduction were also analyzed to optimize the performance of the methods. The nearest neighbor pattern recognition algorithm using the Mahalanobis distance was used to evaluate the performance of the methods. First, a training set of 30 samples per organelle was chosen to train the classifier and to select the best discriminant features. The probability of error was estimated with the leave-one-out method and the results are expressed in percentage of correct classifications. The study shows that features extracted using the spatial gray-level dependence method were the most discriminate ones. The best features set was then applied to a test population of 734 cellular organelles to differentiate the three classes. Correct classifications occurred for 95% of cases, which indicates that it is possible to achieve a semi-automatic analysis of autoradiographic images.
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