Integration of Image Segmentation with Classical L₂ Optimization Theories of Statistics

Music City Center 

Minimum distance estimation methodology based on an empirical distribution function has been popular due to its desirable properties including robustness. Even though the statistical literature is awash with research on the minimum distance estimation, most of it is confined to the theoretical findings: only a few statisticians conducted research on the application of the method to real-world problems. Through this paper, we extend the domain of application of this methodology to various applied fields by providing a solution to a rather challenging and complicated computational problem. The problem this paper tackles is image segmentation, which has been used in various fields. We propose a novel method based on the classical minimum distance estimation theory to solve the image segmentation problem. The performance of the proposed method is then further elevated by integrating it with the "segmenting-together" strategy. We demonstrate that the proposed method combined with the segmenting-together strategy successfully completes the segmentation problem when it is applied to complex images such as magnetic resonance images.

Empirical distribution

Cramer-von Mises

magnetic resonance

minimum distance

segmenting together 

Section on Statistics in Imaging