Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
3-D Image Processing Algorithms
3-D Image Processing Algorithms
Digital Image Processing
Computer Aided Differential Diagnosis of Pulmonary Nodules Using Curvature Based Analysis
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Pattern Recognition in Medical Imaging
Pattern Recognition in Medical Imaging
Proceedings of the 2004 ACM symposium on Applied computing
Pattern Analysis & Applications
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
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This paper analyzes the application of Ripley's K function to characterize lung nodules as malignant or benign in computerized tomography images. The proposed characterization method is based on a selection of measures from Ripley's K function to discriminate between benign and malignant nodules, using stepwise discriminant analysis. Based on the selected measures, a linear discriminant analysis procedure is performed once again in order to predict the classification of each nodule. To evaluate the ability of these features to discriminate the nodules, a set of tests was carried out using a sample of 39 pulmonary nodules, 29 benign and 10 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator's performance. The best setting of the analyzed function in the tested sample presented 70.0% of sensitivity but with 100.0% of specificity and 92.3% of accuracy. Thus, preliminary results of this approach are very promising regarding its contribution to the diagnosis of pulmonary nodules, but it still needs to be tested with larger series and associated to other quantitative imaging methods in order to improve global performance.