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It is generally a problem to select the appropriate preprocessing and feature-extraction technique in most pictorial pattern recognition applications so that an accurate classification is possible. In this paper a class of pictures of medical importance, namely, chest X-ray pictures, is used to test the proposed preprocessing and feature-extraction technique. The technique presented in this paper is applied only to chest X-ray images; however, the same technique could also be applied to a fairly broad class of picture patterns with only some minor modifications. The proposed preprocessing technique, which utilizes the local and global information of the picture patterns, is to extract the lung boundary. The lung field is then enclosed by a polygon which is the piecewise linear approximation of the lung boundary. The set of texture features, which are the average of some local property measures, is has then extracted in this approximated lung area. The proposed technique been tested on two sets of X-ray picture classesÂ¿one with abnormalities caused by a known disease and the others with abnormalities caused by some unkown effects in the lung region. The classification results presented in this paper show the feasibility of the proposed pictorial pattern recognition system in effectively screening out the abnormal pictures without human intervention.