Nonlinear energy operator (NEO) gives the estimate of energy content of a linear oscillator. This has been used to quantify the AM-FM modulating signals present in a sinusoid. In this paper, we give a new interpretation of NEO and extend its use in stochastic signals. We show that NEO accentuates the high-frequency content. This instantaneous nature of NEO and its very low computational burden make it an ideal tool for spike detection. The efficacy of the proposed method has been tested with simulated signals as well as with real electroencephalograms (EEG's).

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Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy "1" and "2" as benign and "4" and "5" as malignant), configuration 2 (composite rank of malignancy "1","2", and "3" as benign and "4" and "5" as malignant), and configuration 3 (composite rank of malignancy "1" and "2" as benign and "3","4" and "5" as malignant). The performance of the classification is evaluated in terms of area (A z) under the receiver operating characteristic curve. The A z achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.

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We present the results of Monte Carlo simulations of a percolation model with long-range correlations in two and three dimensions. The correlations are generated by a fractional Brownian motion. The nature of the percolation transition in this model is discussed. The percolation thresholds and the critical exponents of the model are calculated. The exponents are found to be mostly nonuniversal and dependent on a parameter that characterizes the nature of the correlations. Some possible applications of the model are discussed in detail, including flow in field-scale porous media ͑with megascopic disorder͒ with a given permeability distribution, and estimating their effective permeability, and transport and dispersion in geological formations and explaining the anomalous and nonuniversal behavior of the dispersivity that has been observed in many field-scale experiments, in terms of the nonuniversal properties of our model. ͓S1063-651X͑96͒08109-3͔PACS number͑s͒: 47.55.Mh, 64.60.Ak

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In medio-lateral oblique view of mammogram, pectoral muscle may sometimes affect the detection of breast cancer due to their similar characteristics with abnormal tissues. As a result pectoral muscle should be handled separately while detecting the breast cancer. In this paper, a novel approach for the detection of pectoral muscle using average gradient-and shape-based feature is proposed. The process first approximates the pectoral muscle boundary as a straight line using average gradient-, position-, and shape-