Journal Title : International Journal of Modern Trends in Engineering and Science
Volume 03 Issue 06 2016
ISSN no: 2348-3121
Page no: 75-79
Abstract – CAD (computer-aided detection) schemes for PE detection usually include a difficult and time- consuming step of classifying the large number of initially detected suspicious regions as the TP(true- positive) and FP(false-positive) PE(pulmonary embolism) lesions. The proposed CAD scheme consists of five basic steps: 1) lung segmentation; 2) PE candidate extraction using an intensity mask and tobogganing region growing; 3) PE andiate feature extrac-tion; 4) false-positive (FP) reduction using an artificial neural network (ANN); and 5) a multifeature- based k-nearest neighbor for positive/negative classification. This paper proposes a new feature selection method based on FIsher criterion and Genetic optimization,called FIG for short, to tackle the CISL recognition problem. In our FIG feature selection method, the Fisher criterion is applied to evaluate feature subsets, based on which a genetic optimization algorithm is developed to find out an optimal feature subset from the candidate features.
Keywords— computer aided detection,feature selection,common CT imaging signs of lung diseases,artificial neural network
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