Application of Parametric Statistical Weights in CAD imaging systems

PURPOSE: To propose a method for Parametric Statistical Weights (PSW) estimations and analyze its statistical impact in Computer-Aided Diagnosis Imaging Systems based on a Relative Similarity (CADIRS) classification approach. MATERIALS AND METHODS: A Multifactor statistical method was developed and applied for Parametric Statistical Weights calculations in CADIRS. The implemented PSW method was used for statistical estimations of PSW impact when applied to a clinically validated breast ultrasound digital database of 332 patients' cases with biopsy proven findings. The method is based on the assumption that each parameter used in Relative Similarity (RS) classifier contributes to the deviation of the diagnostic prediction proportionally to the normalized value of its coefficient of multiple regression. The calculated by CADIRS Relative Similarity values with and without PSW were statistically estimated, compared and analyzed (on subset of cases) using classic Receiver Operator Characteristic (ROC) analysis methods. RESULTS: When CADIRS classification scheme was augmented with PSW the Relative Similarity the calculated values were 2-5% higher in average. Numeric estimations of PSW allowed decomposition of statistical significance for each component (factor) and its impact on similarity to the diagnostic results (biopsy proven). CONCLUSION: Parametric Statistical Weights in Computer-Aided Diagnosis Imaging Systems based on a Relative Similarity classification approach can be successfully applied in an effort to enhance overall classification (including scoring) outcomes. For the analyzed cohort of 332 cases the application of PSW increased Relative Similarity to the retrieved templates with known findings by 2-5% in average.