Search results for "Unsharp masking"
showing 6 items of 6 documents
Pervasive access to MRI bias artifact suppression service on a grid.
2009
Bias artifact corrupts magnetic resonance images in such a way that the image is afflicted by illumination variations. Some of the authors proposed the Exponential Entropy Driven - Homomorphic Unsharp Masking (E2D-HUM) algorithm that corrects this artifact without any a priori hypothesis about the tissues or the Magnetic Resonance image modality. Moreover, E2D-HUM does not care about the body part under examination and does not require any particular training task. People who want to use this algorithm, which is Matlab-based, have to set their own computers in order to execute it. Furthermore, they have to be Matlab-skilled to exploit all the features of the algorithm. In our work we propos…
Rank-order and morphological enhancement of image details with an optoelectronic processor.
2010
In all-optical processors, enhancement of image details is the result of high-pass filtering. We describe an optoelectronic processor in which detail enhancement results from the digitally calculated difference between an original input image and its low-pass filtered version. The low-pass filtering is realized through the rank-order median and the morphological opening and closing operations calculated by use of the optical convolver. It is shown that the normalized difference between the morphological white and black top hats enhances bright and dark image details analogously to the rank-order unsharp masking.
Illumination correction on biomedical images
2014
RF-Inhomogeneity Correction (aka bias) artifact is an important re- search field in Magnetic Resonance Imaging (MRI). Bias corrupts MR images alter- ing their illumination even though they are acquired with the most recent scanners. Homomorphic Unsharp Masking (HUM) is a filtering technique aimed at correcting illumination inhomogeneity, but it produces a halo around the edges as a side effect. In this paper a novel correction scheme based on HUM is proposed to correct the artifact mentioned above without introducing the halo. A wide experimentation has been performed on MR images. The method has been tuned and evaluated using the simulated Brainweb image database. In this framework, the ap…
Volumetric Bias Correction
2007
This paper presents a method to suppress the bias artifact, also known as RF-inhomogeneity, in Magnetic Resonance Imaging (MRI). This artifact produces illumination variations due to magnetic field fluctuations of the device. In the latest years many works have been devoted to face this problem. In this work we present the 3D version of a new approach to bias correction, which is called Exponential Entropy Driven Homomorphic Unsharp Masking (E2D-HUM). This technique has been already presented by some of the authors for the 2D case only. The description of the whole method is detailed, and some experimental results are reported.
Morphological exponential entropy driven-HUM.
2006
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 (…
Frequency Determined Homomorphic Unsharp Masking Algorithm on Knee MR Images
2005
A very important artifact corrupting Magnetic Resonance (MR) Images is the RF inhomogeneity, also called Bias artifact. The visual effect produced by this kind of artifact is an illumination variation which afflicts this kind of medical images. In literature a lot of works oriented to the suppression of this artifact can be found. The approaches based on homomorphic filtering offer an easy way to perform bias correction but none of them can automatically determine the cut-off frequency. In this work we present a measure based on information theory in order to find the frequency mentioned above and this technique is applied to MR images of the knee which are hardly bias corrupted.