  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 the research on the
minimum distance estimation, the most of it is confined to the theoretical
findings: only 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 an 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 the complex, real images such as
magnetic resonance images.
