High-Quality 3D Medical Imaging by Wavelet Filters with Reduced Coefficients Bit-Width

2019 ◽  
Author(s):  
Nikolai I. Chervyakov ◽  
Pavel A. Lyakhov ◽  
Nikolai N. Nagornov ◽  
Maria V. Valueva
Keyword(s):  
Medical Imaging ◽  
High Quality ◽  
Wavelet Filters

scholarly journals Radiographers’ Knowledge Versus Practice about Methods of Attracting Pediatric Corporation in Medical Imaging Departments

2021 ◽  
Author(s):  
Farideh Pak ◽  
Mohadese Estaji ◽  
Behnaz Rezaei ◽  
Vahid Vaezzadeh
Keyword(s):  
Medical Imaging ◽  
Analytical Study ◽  
Absorbed Dose ◽  
Verbal Communication ◽  
The Other ◽  
Continuous Training ◽  
High Quality ◽  
Imaging Department ◽  
Knowledge And Practice

Purpose: The goal of this study was to evaluate radiographers’ knowledge and practice on different methods for attracting pediatrics’ cooperation in medical imaging departments for achieving high-quality images without repetition and minimum absorbed dose. Materials and Methods: For conducting this descriptive-analytical study, a researcher-made questionnaire, including two parts of radiographer knowledge and practical methods, which were applied as a routine in the departments for reduction of pediatrics’ stress, was distributed between radiographers. Results: The results revealed that verbal justification was declared as the most efficient way of informing the parents as compared to the other methods. Establishing verbal communication is the most practical way of engaging the child. Meanwhile, application of immobilization tools, justification of parents by the admission staff, playing music was used, respectively. Conclusion: Considering these findings, there is a need to equip the imaging department with the appropriate facilities, perform continuous training of radiographers to increase the practice of different techniques and tools.

Innovative Monochromatic X‐ray Source for High Quality and Low Dose Medical Imaging

2020 ◽  
Author(s):  
Eric Silver ◽  
Seth Shulman ◽  
Madan M. Rehani
Keyword(s):  
Medical Imaging ◽  
Low Dose ◽  
High Quality ◽  
X Ray

scholarly journals Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

Micromachines ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1418
Author(s):  
Yue Yu ◽  
Kun She ◽  
Jinhua Liu
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Super Resolution ◽  
Frequency Separation ◽  
Wavelet Coefficients ◽  
Low Resolution ◽  
High Quality ◽  
Attention Network ◽  
Resolution Image ◽  
Image Super Resolution

Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.

Research Status in the Fusion and Enrichment of Medical Imaging for High Quality Diagnosis and Treatment (FERMI) Project

2013 ◽  
Vol 3 (1) ◽  
pp. 51-58
Author(s):  
Hideaki Haneishi ◽  
Tadashi Yamaguchi ◽  
Ryoichi Nakamura ◽  
Toshiya Nakaguchi ◽  
Mikio Suga ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Diagnosis And Treatment ◽  
High Quality ◽  
Research Status ◽  
Quality Diagnosis

scholarly journals Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

2020 ◽  
Vol 10 (4) ◽  
pp. 1223 ◽  
Author(s):  
Nikolay Chervyakov ◽  
Pavel Lyakhov ◽  
Nikolay Nagornov
Keyword(s):  
Wavelet Transform ◽  
Medical Imaging ◽  
Integrated Circuits ◽  
Discrete Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Maximum Error ◽  
Quantization Noise ◽  
Discrete Wavelet ◽  
Wavelet Filters ◽  
Images Processing

Denoising and compression of 2D and 3D images are important problems in modern medical imaging systems. Discrete wavelet transform (DWT) is used to solve them in practice. We analyze the quantization noise effect in coefficients of DWT filters for 3D medical imaging in this paper. The method for wavelet filters coefficients quantizing is proposed, which allows minimizing resources in hardware implementation by simplifying rounding operations. We develop the method for estimating the maximum error of 3D grayscale and color images DWT with various bits per color (BPC). The dependence of the peak signal-to-noise ratio (PSNR) of the images processing result on wavelet used, the effective bit-width of filters coefficients and BPC is revealed. We derive formulas for determining the minimum bit-width of wavelet filters coefficients that provide a high (PSNR ≥ 40 dB for images with 8 BPC, for example) and maximum (PSNR = ∞ dB) quality of 3D medical imaging by DWT depending on wavelet used. The experiments of 3D tomographic images processing confirmed the accuracy of theoretical analysis. All data are presented in the fixed-point format in the proposed method of 3D medical images DWT. It is making possible efficient, from the point of view of hardware and time resources, the implementation for image denoising and compression on modern devices such as field-programmable gate arrays and application-specific integrated circuits.

Automated High Quality Isosurface Modeling Technique for Iterative Two-Phase Problems

2014 ◽  
Vol 553 ◽  
pp. 818-823
Author(s):  
Che Cheng Chang ◽  
Qing Li
Keyword(s):  
Medical Imaging ◽  
Computational Cost ◽  
Modeling Technique ◽  
High Quality ◽  
Two Phase ◽  
Element Analysis ◽  
Seamless Integration ◽  
Rapid Construction ◽  
Iterative Procedures ◽  
Engineering Modeling

While smooth isosurface and the subsequent body mesh construction is a well-developed modeling technique and widely used in medical imaging and engineering modeling, it is rarely performed in transient analysis and other iterative procedures due to relatively high computational cost. Voxelized modeling is often used as an alternative for simplicity at a cost of numerical accuracy. To overcome this problem, an isosurface modeling technique is developed in this paper to enable its seamless integration into iterative processes. This approach involves a rapid construction of closed isosurfaces using the Marching Cubes methods and a selective clean-up operation to smooth the surface mesh. This technique generates high quality isosurface meshes with clearly defined 3D domains and boundaries, which in turn provide a suitable foundation for the finite element analysis of two-phase problems. Its robustness, flexibility and suitability for applications in medical imaging and topology optimization are also demonstrated in this paper.

Defining of the Minimum Wavelet Filter Coefficients Bit-Width for 3D Medical Imaging

2021 ◽  
Vol 27 (8) ◽  
pp. 425-434
Author(s):  
N. N. Nagornov ◽  
Keyword(s):  
Medical Imaging ◽  
Integrated Circuits ◽  
Visual Information ◽  
Linear Time ◽  
Quantization Noise ◽  
Discrete Wavelet ◽  
Wavelet Filter ◽  
High Quality ◽  
3D Images ◽  
Linear Time Invariant

Medical imaging uses a variety of modalities to provide visual information about a patient. Various methods are used to process this data. Many of them are based on discrete wavelet transform (DWT). Its use will allow effective denoising and compression of 2D and 3D images. This paper proposes a new approach to linear time-invariant wavelet filtering using quantized filter coefficients when using which the computational errors have different signs and allow to partially compensate each other as a result of which the processed image is of high quality. The analysis of the quantization noise of the direct multilevel DWT filter coefficients is carried out. The derived formulas demonstrate the relationship between the quantization accuracy of these coefficients and the processing quality of digital 3D images. The derived formulas for calculating the minimum accuracy of the wavelet filter coefficients representation in the computing devices memory allow minimizing the effect of quantization noise on the result of 3D images processing. Modelling of 3D medical tomographic images DWT processing showed that a decrease in the ratio of the average voxel brightness to the maximum allowable value with increasing color depth of images leads to faster achievement of high quality compared to the results of theoretical analysis with an increase in the value of the scaling degree of the wavelet filter coefficients. The obtained theoretical and practical results open up the possibility for reducing the computational complexity of software and hardware implementation of wavelet processing of 3D medical visual data on modern microelectronic devices (field-programmable gate arrays, application-specific integrated circuits, etc.).

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