6533b836fe1ef96bd12a0c27

RESEARCH PRODUCT

Morphological exponential entropy driven-HUM.

subject

description

This paper presents an improvement to the Ex- ponential Entropy Driven - Homomorphic Unsharp Masking (E 2 D − HUM ) algorithm devoted to illumination artifact sup- pression on Magnetic Resonance Images. E 2 D−HUM requires a segmentation step to remove dark regions in the foreground whose intensity is comparable with background, because strong edges produce streak artifacts on the tissues. This new version of the algorithm keeps the same good properties of E 2 D − HUM without a segmentation phase, whose parameters should be chosen in relation to the image. I. INTRODUCTION Most of the studies on illumination correction found in literature are oriented to brain (18) magnetic resonance images (mri). The knee mri are strongly affected by this artifact due to the particular coil configuration of specialized devices for upper and lower limbs. Two main approaches are used to suppress illumination inhomogeneity. The first kind of algorithms like(7)(13)(5) modify the expectation maximization or fuzzy c-means (10)(4)(16). The original minimizing functional is changed to take into account the artifact corrupting the images, new parameters are introduced decreasing the original performance. The algorithms like (11)(8)(12)(9) suppress the artifact making use of a particular probe inserted into the device to produce a correcting bias surface, other methods discover the artifact using methods based on information theory (14)(15), preconditioned gra- dient algorithm (3), polynomial fitting (2) or an "a priori" model (17). The paper is organized as follows: Section 2 describes briefly E 2 D − HUM ; Section 3 describes ME 2 D − HUM ; Section 4 perform a comparison among ME 2 D − HUM , E 2 D − HUM , SPM2 (20); Section 5 is devoted to measures; Section 6 describes briefly the dataset; Section 7 reports some conclusions.