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CT Value Histogram-Based Classification Framework

Volume measurement is one of several approaches to the management of pulmonary nodules detected by CT scanning [117]. This approach often encounters cases where nodules are volumetrically stable in spite of internal CT value variation. These authors have attempted to develop a 3D computerized method for evaluating the volumetric distribution of CT values within pulmonary nodules. We found that the analysis of CT histograms is a potentially useful method for the quantitative classification of pulmonary nodules without requiring measurement of the proportion of nonsolid and solid components [102, 105, 106]. In [102], we developed a five-category classification approach based on the analysis of CT value histograms and investigated the impact of nodule segmentation on classification and the effect of classification on disease-free survival. We also extended the approach to compute a histogram-based score of recurrence risk to track time- interval changes in pulmonary nodules via variational Bayesian mixture modeling for the features obtained from analysis of CT histograms [105, 106]. The key contribution to the computational anatomical models is to represent the internal structure of pulmonary nodules for computing a histogram-based risk score that correlates with prognostic factors. The framework consists of five steps: (1) nodule segmentation, (2) computation of a CT histogram, (3) nodule categorization by applying the variational Bayesian model to cluster CT histograms, (4) computation of the histogram-based risk score by using the combination of the contribution that each category makes to describing the nodule [105, 106], and (5) prognostic prediction using the histogram-based risk score. A schematic overview of the prognostic prediction approach is shown in Fig. 4.5.

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