  Following the presentation and proof of the hypothesis that image features
are particularly perceived at points where the Fourier components are maximally
in phase, the concept of phase congruency (PC) is introduced. Subsequently, a
two-dimensional multi-scale phase congruency (2D-MSPC) is developed, which has
been an important tool for detecting and evaluation of image features. However,
the 2D-MSPC requires many parameters to be appropriately tuned for optimal
image features detection. In this paper, we defined a criterion for parameter
optimization of the 2D-MSPC, which is a function of its maximum and minimum
moments. We formulated the problem in various optimal and suboptimal
frameworks, and discussed the conditions and features of the suboptimal
solutions. The effectiveness of the proposed method was verified through
several examples, ranging from natural objects to medical images from patients
with a neurological disease, multiple sclerosis.
