A Review on MR Image Intensity Inhomogeneity Correction

Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed.

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MR image intensity inhomogeneity correction

Journal of Physics Conference Series ◽

10.1088/1742-6596/574/1/012125 ◽

2015 ◽

Vol 574 ◽

pp. 012125

Author(s):

Mirela (Vişan) Pungǎ ◽

Simona Moldovanu ◽

Luminita Moraru

Keyword(s):

Intensity Inhomogeneity ◽

Mr Image ◽

Image Intensity ◽

Inhomogeneity Correction

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Kernel Graph Cuts Segmentation for MR Images with Intensity Inhomogeneity Correction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.938 ◽

2013 ◽

Vol 333-335 ◽

pp. 938-943

Author(s):

Qing Luo ◽

Wen Jian Qin ◽

Jia Gu

Keyword(s):

Image Data ◽

Graph Cuts ◽

Intensity Inhomogeneity ◽

Mr Images ◽

Mr Image ◽

Fcm Algorithm ◽

The Real ◽

Inhomogeneity Correction ◽

Segmentation Approach ◽

Correction Step

Since the phenomena of intensity inhomogeneity in MR images are prominent and adversely affect quantitative image analysis .In this paper ,we propose a novel magnetic resonance (MR) image segmentation approach based on the kernel graph cuts technique .Because of automatic multiregion segmentation and global energy minimization ,the kernel graph cuts method can be applied to many kinds of images segmentation ,such as MR images and so on . To reduce or eliminate intensity inhomogeneity in MR images ,we add the intensity inhomogeneity correction step which is based on fuzzy c-means (FCM) algorithm before the segment procedure .Firstly , the real MR image data obtained after bias corrected by FCM algorithm .Secondly ,we segment the real MR image data by kernel graph cuts method .Experiments show that the kernel graph cuts method with intensity inhomogeneity correction have a better segment result in accuracy and over-segmentation .

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Simulations of high-resolution images of amorphous silicon films

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014419x ◽

1986 ◽

Vol 44 ◽

pp. 544-545

Author(s):

G. Y. Fan ◽

J. M. Cowley

Keyword(s):

Electron Microscope ◽

High Frequency ◽

Fourier Transformation ◽

Amorphous Materials ◽

Low Frequency ◽

Chromatic Aberration ◽

Structure Information ◽

Frequency Components ◽

High Resolution Images ◽

Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

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ANALYSIS OF STAND RESEARCH RESULTS OF AXIAL DYNAMIC FORCE IN DRILLING BY THREE ROLLER CONE BIT

PRECARPATHIAN BULLETIN OF THE SHEVCHENKO SCIENTIFIC SOCIETY Number ◽

10.31471/2304-7399-2019-1(53)-81-93 ◽

2019 ◽

pp. 81-93

Author(s):

В. М. Мойсишин ◽

M. V. Lyskanych ◽

R. A. Zhovniruk ◽

Ye. P. Majkovych

Keyword(s):

Low Frequency ◽

Maximum Energy ◽

Dynamic Force ◽

Low Frequency Oscillation ◽

Drilling Tool ◽

Rock Destruction ◽

Frequency Components ◽

Harmonic Components ◽

Experimental Values ◽

Harmful Effects

The purpose of the proposed article is to establish the causes of oscillations of drilling tool and the basic laws of the distribution of the total energy of the process of changing the axial dynamic force over frequencies of spectrum. Variable factors during experiments on the classical plan were the rigidity of drilling tool and the hardness of the rock. According to the results of research, the main power of the process of change of axial dynamic force during drilling of three roller cone bits is in the frequency range 0-32 Hz in which three harmonic frequency components are allocated which correspond to the theoretical values of low-frequency and gear oscillations of the chisel and proper oscillations of the bit. The experimental values of frequencies of harmonic components of energy and normalized spectrum as well as the magnitude of the dispersion of the axial dynamic force and its normalized values at these frequencies are presented. It has been found that with decreasing rigidity of the drilling tool maximum energy of axial dynamic force moves from the low-frequency oscillation region to the tooth oscillation area, intensifying the process of rock destruction and, at the same time, protecting the tool from the harmful effects of the vibrations of the bit. Reducing the rigidity of the drilling tool protects the bit from the harmful effects of the vibrations generated by the stand. The energy reductions in these fluctuations range from 47 to 77%.

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A Novel Adaptive PET/CT Image Fusion Algorithm

Current Bioinformatics ◽

10.2174/1574893613666180704153946 ◽

2019 ◽

Vol 14 (7) ◽

pp. 658-666

Author(s):

Kai-jian Xia ◽

Jian-qiang Wang ◽

Jian Cai

Keyword(s):

Lung Cancer ◽

Image Fusion ◽

High Frequency ◽

Malignant Tumors ◽

Low Frequency ◽

Combination Method ◽

Ct Image ◽

Fusion Algorithm ◽

Pet Ct ◽

Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

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Underwater Image Enhancement Method Combining color and Luminance

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096513999201026225829 ◽

2020 ◽

Vol 13 ◽

Author(s):

ZHAO Baiting ◽

WANG Feng ◽

JIA Xiaofen ◽

GUO Yongcun ◽

WANG Chengjun

Keyword(s):

Image Enhancement ◽

Color Space ◽

Low Frequency ◽

Brightness Enhancement ◽

Color Distortion ◽

Underwater Image ◽

Enhancement Method ◽

Frequency Components ◽

Image Contour ◽

Objective Indicators

Background:: Aiming at the problems of color distortion, low clarity and poor visibility of underwater image caused by complex underwater environment, a wavelet fusion method UIPWF for underwater image enhancement is proposed. Methods:: First of all, an improved NCB color balance method is designed to identify and cut the abnormal pixels, and balance the color of R, G and B channels by affine transformation. Then, the color correction map is converted to CIELab color space, and the L component is equalized with contrast limited adaptive histogram to obtain the brightness enhancement map. Finally, different fusion rules are designed for low-frequency and high-frequency components, the pixel level wavelet fusion of color balance image and brightness enhancement image is realized to improve the edge detail contrast on the basis of protecting the underwater image contour. Results:: The experiments demonstrate that compared with the existing underwater image processing methods, UIPWF is highly effective in the underwater image enhancement task, improves the objective indicators greatly, and produces visually pleasing enhancement images with clear edges and reasonable color information. Conclusion:: The UIPWF method can effectively mitigate the color distortion, improve the clarity and contrast, which is applicable for underwater image enhancement in different environments.

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Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

International Journal of Image and Graphics ◽

10.1142/s0219467821500236 ◽

2020 ◽

pp. 2150023

Author(s):

Priya R. Kamath ◽

Kedarnath Senapati ◽

P. Jidesh

Keyword(s):

High Frequency ◽

Low Frequency ◽

Relevant Information ◽

Detection Algorithm ◽

Two Dimensional ◽

Sar Images ◽

S Transform ◽

Processing Step ◽

Frequency Components ◽

Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

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Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

Acta Epileptologica ◽

10.1186/s42494-020-00035-9 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Shijun Yang ◽

Bin Wang ◽

Xiong Han

Keyword(s):

Antiepileptic Drugs ◽

Statistical Models ◽

Prediction Models ◽

External Validation ◽

Machine Learning Algorithms ◽

Treatment Withdrawal ◽

Patient Treatment ◽

Recent Developments ◽

Progression Of Disease ◽

Patients With Epilepsy

AbstractAlthough antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline.

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Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

Journal of Geophysics and Engineering ◽

10.1093/jge/gxz062 ◽

2019 ◽

Vol 16 (6) ◽

pp. 1017-1031 ◽

Cited By ~ 5

Author(s):

Yong Hu ◽

Liguo Han ◽

Rushan Wu ◽

Yongzhong Xu

Keyword(s):

Frequency Domain ◽

Seismic Data ◽

Waveform Inversion ◽

Low Frequency ◽

Full Waveform Inversion ◽

Local Correlation ◽

Time Frequency ◽

Model Dependence ◽

Full Waveform ◽

Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

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