27 February 2009 Knowledge based optimum feature selection for lung nodule diagnosis on thin section thoracic CT
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 726036 (2009) https://doi.org/10.1117/12.812926
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
An approach for optimum selection of lung nodule image characteristics in the feature domain is presented. This was applied to the classification module in the CAD system with data that was extracted from 42 ROI's of the 38 cases with an effective diameter of 3 to 8.5mm. 11 fundamental features were computed on the basis of dimensionality and image characteristics. The relation between the represented features of the 4 radiologists and the computed features was mapped using non-parametric correlation coefficients, multiple regression analysis and principle component analysis (PCA). Malignant and benign modules were classified based on the artificial neural network (ANN) to confirm the hypothesis from the mapping analysis. From the computed features and the radiologist's annotations, correlation coefficients between 0.2693 and 0.5178 were obtained. A combination of analyses namely regression, PCA, correlation and ANN were used to select optimum features. This resulted in F-test values of 0.821 and 0.643 for malignant and benign nodules respectively. The study of the relationship between the features and the weightage towards each of the representative classes resulted in optimum feature input for a CAD system. A composite analysis derived from correlation, PCA, multiple regression and the classification algorithm, collectively termed as the knowledge base, was used arrive at an "optimum" set of lung nodule features.
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Ravi K. Samala, Ravi K. Samala, Wilfrido A. Moreno, Wilfrido A. Moreno, Danshong Song, Danshong Song, Yuncheng You, Yuncheng You, Wei Qian, Wei Qian, } "Knowledge based optimum feature selection for lung nodule diagnosis on thin section thoracic CT", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726036 (27 February 2009); doi: 10.1117/12.812926; https://doi.org/10.1117/12.812926
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