Mammographic Computer-Assisted Diagnosis using Computational Statistics Pattern Recognition

Abstract Research begun for target identification utilizing pattern recognition has been applied to mammographic computer-assisted diagnosis. The research has utilized the discipline of computational statistics. Feature extraction based on fractals and incorporating segmentation boundaries led to probability density estimation and classification based on discriminant analysis. The results of applying these techniques to mammography are very promising and are reported herein. The results of these limited mammographic studies are discussed in their own light and in comparison with other's work.