A novel approach to nodule feature optimization on thin section thoracic CT.
OBJECTIVE
An analysis for the optimum selection of image features in feature domain to represent lung nodules was performed, with implementation into a classification module of a computer-aided diagnosis system.


METHODS
Forty-two regions of interest obtained from 38 cases with effective diameters of 3 to 8.5 mm were used. On the basis of image characteristics and dimensionality, 11 features were computed. Nonparametric correlation coefficients, multiple regression analysis, and principal-component analysis were used to map the relation between the represented features from four radiologists and the computed features. An artificial neural network was used for the classification of benign and malignant nodules to test the hypothesis obtained from the mapping analysis.


RESULTS
Correlation coefficients ranging from 0.2693 to 0.5178 were obtained between the radiologists' annotations and the computed features. Of the 11 features used, three were found to be redundant when both nodule and non-nodule cases were used, and five were found redundant when nodule or non-nodule cases were used. Combination of analysis from correlation coefficients, regression analysis, principal-component analysis, and the artificial neural network resulted in the selection of optimum features to achieve F-test values of 0.821 and 0.643 for malignant and benign nodules, respectively.


CONCLUSIONS
This study demonstrates that for the optimum selection of features, each feature should be analyzed individually and collectively to evaluate the impact on the computer-aided diagnosis system on the basis of its class representation. This methodology will ultimately aid in improving the generalization capability of a classification module for early lung cancer diagnosis.
Samala
Ravi
R
College of Engineering, University of South Florida, Tampa, FL, USA.
Moreno
Wilfrido
W
You
Yuncheng
Y
Qian
Wei
W
D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*