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Proceedings Paper

Knowledge based optimum feature selection for lung nodule diagnosis on thin section thoracic CT
Author(s): Ravi K. Samala; Wilfrido A. Moreno; Danshong Song; Yuncheng You; Wei Qian
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Paper 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.

Paper Details

Date Published: 27 February 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726036 (27 February 2009); doi: 10.1117/12.812926
Show Author Affiliations
Ravi K. Samala, Univ. of South Florida (United States)
Wilfrido A. Moreno, Univ. of South Florida (United States)
Danshong Song, Univ. of South Florida (United States)
Yuncheng You, Univ. of South Florida (United States)
Wei Qian, Florida Institute of Technology (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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