Abstract
A method for the preliminary processing of MRI images of the heart that allows for the elimination of fluctuation and impulse noise from useful signals is proposed. These types of noise are due to the regular geometric structure of the photoelectric elements of the MRI scanner matrix and the structure of the signal transmission channel. The aim of this work is to develop a comprehensive mathematical model for eliminating noise in the signal of an MRI scanner. In this work, mathematical models of linear and median filtering of impulse noise, fluctuation, and geometric noise are implemented. The mathematical models consist of the combined use of linear and median filters for recording MRI images of the heart. In the experiments, real MRI images of the heart from six patients with different diseases were used after noise was added to them. We were able to eliminate the impulse noise, geometric noise, and fluctuation noise in the MRI images by applying our filtering techniques. The filtering technique not only removed the noise, but also increased the contrast of the cancerous volumetric heterogeneous formations in the heart region.
Highlights
Magnetic resonance imaging (MRI) images usually suffer from different types of noise, such as Gaussian noise, Poisson noise, speckle noise, etc. [1]
A method for the preliminary processing of MRI images of the heart that allows for the elimination of fluctuation and impulse noise from useful signals is proposed
Clinical testing of models for eliminating noise in MRI images of the heart was performed to diagnose cancerous tumors in the heart, which form as a result of the progression of an underlying disease
Summary
Magnetic resonance imaging (MRI) images usually suffer from different types of noise, such as Gaussian noise, Poisson noise, speckle noise, etc. [1]. Examples of such methods are the full variation method and the median method of filtering one-dimensional signals [1,2,3,4,5,6,7,8,9,10,11,12], which are known to be effective approaches for eliminating Gaussian and Poisson noise [13,14,15,16] Both types of noise (Poisson and Gaussian) have been well-studied, but their combination in a recorded signal [17], which is often present in biomedical signals and electron microscopy images, is important [18,19].
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