scholarly journals Evaluation of blurring and noise of a medical image using a digital phantom

2021 ◽  
Vol 12 (3) ◽  
pp. 423-426
Author(s):  
Cheolpyo Hong
Keyword(s):  
Standard Deviation ◽  
Line Profile ◽  
Medical Image ◽  
Imaging System ◽  
Original Form ◽  
Black And White ◽  
Image Blurring ◽  
Blurred Image ◽  
Digital Phantom ◽  
Gaussian Blur

Blurring and noise are an essential characteristic of a medical image on the imaging system. This study shows the characteristics of blurring and noise of a medical image using a digital phantom. A square-shaped digital phantom was produced with pixels that consist of black and white. The line profile was selected on a binary digital image. An image with noise added was generated and a corresponding line profile was also drawn. The degree of noise was increased using the gaussian noise value. The blurring images obtained by applying gaussian blur to a digital phantom was produced similarities to real images. Also, the degree of blurring was increased using the gaussian blur value. As noise increased, the standard deviation of pixels inside and background the object also increased. However, the boundary of the object was retained. As image blurring increased, the boundary of the object was not clearly distinguished. However, the standard deviation of pixels inside and background the object was retained. When extreme noise and blurring are increased, the resulting images are different. For adding noise, the shape is visually maintained. However, the blurred image does not maintain a square shape. Therefore, it is shown that blurring due to movement of object cannot maintain original form. In the image processing method, the reduction of noise is achieved through blur processing. The noise was reduced through blur processing in the image with noise. The degree of noise decreased, but the ambiguity of the boundary increased.

scholarly journals Measurement of Slice Thickness of a Medical Image using a Wedge Phantom

2020 ◽  
Vol 9 (4) ◽  
pp. 327-329
Keyword(s):  
Line Profile ◽  
Imaging System ◽  
Medical Images ◽  
Wedge Angle ◽  
Gaussian Function ◽  
Thickness Measurement ◽  
Slice Thickness ◽  
Line Profiles ◽  
Slice Profile ◽  
Digital Phantom

Slice thickness measurement is an essential parameter of performance evaluation for the medical imaging system. This study demonstrates the characteristics of slice thickness measurement for medical images using a wedge digital phantom. A wedge-shaped digital phantom was generated and the ideal edge response function (ERF) was extracted from line profile in single slice. The corresponding slice profile was calculated by the derivative of ERF. The wedge phantom obtained by applying gaussian convolving to a digital phantom was also generated to produce similarities to real medical images. Unlike an ideal slice profile, it was estimated by the full width half maximum (FWHM) of the Gaussian function fitting. In addition, we evaluate the effect of background noise and wedge angle for the wedge phantom. The estimated FWHM of the image with noise added was increased by 10.4% compared to the image without noise. However, the FWHM from the line profiles averaging on the noise-added image was estimated by 0.2% reduction than the noise-free image. The line profiles averaging improves the accurate measurement of slice thickness by decreasing the noise. Despite the wedge angle changing from 45 to 30 degrees, the resulting FWHM was estimated to have less than 1% difference. However, the length of the line profile to be acquired should be increased as the wedge angle increases.

Restoration of Motion Blurred Image Based on PSO Combine Wiener Filter in Ship Imaging System

2014 ◽  
Vol 1006-1007 ◽  
pp. 739-742
Author(s):  
Hui Xuan Fu ◽  
Yu Chao Wang ◽  
Xun Su
Keyword(s):  
Imaging System ◽  
Search Algorithm ◽  
Wiener Filter ◽  
Optimal Solution ◽  
Parameter Selection ◽  
Improve Image Quality ◽  
Blurred Image ◽  
Blurred Images ◽  
Ringing Effect

Ship internal equipment vibration will cause the imaging system platform vibration, resulting in blurred images. Wiener Filter is often used to restore the motion blurred image. The principle of the method expects to minimize the mean square error between the restore image and original image. However, this method has some constrains, if parameter selection improper, it generates ringing effect easily. Usually, most users select parameter by rule of thumb, so they frequently fail to generate the optimal solution. In order to get high quality restore image, eliminate the ringing effect, a new approach based on particle swarm optimization (PSO) Wiener Filter was proposed, which automatically adjusts the parameter for Wiener Filter, this method seek the optimal solution by transferring information between individuals and information sharing, which is a highly efficient parallel search algorithm, insuring the accuracy of parameter selection, effectively reducing the ringing effect after image restoration, improve image quality of restoration.

scholarly journals Optical density analysis of standard phantom for mammography quality control

2018 ◽  
Vol 7 (3.3) ◽  
pp. 22
Author(s):  
Mi. Hwa Lee ◽  
Hong Ryang Jung ◽  
Cheong Hwan Lim ◽  
Mi Soon Park ◽  
Ki. Jeong Kim ◽  
...  
Keyword(s):  
Medical Image ◽  
Visual Inspection ◽  
Concentration Difference ◽  
Black And White ◽  
Optic Density ◽  
Significant Difference ◽  
Breast Phantom ◽  
Level Information ◽  
Density Analysis ◽  
Mean Concentration

Background/Objectives: The objective of this study was to analyze optic density of dummy lesions in breast phantom by mammography and understand whether the objectivity of visual inspections on dummy lesions upon appropriate and inappropriate decisions was correct quantitatively.Methods/Statistical analysis: Study subjects were 165 phantom images in 74 hospitals nationwide that passed the test of special medical equipment by Korean Institute for Accreditation of Medical Image from May 2016 to April 2017. Min (A), Max (B), Mean (C), and StdDev (D) were measured using color level information. Fibers, specks, and masses divided dummy lesions determined as appropriate or inappropriate in 165 images. For each divided dummy lesion, Min, Max, Mean concentration, and concentration difference summarized it.Findings: There was a significant difference in Max concentration (B) between dummy lesions of 9 (specks 3) and 10 (specks 4). In dummy lesion 12 (mass 1), there was no significant difference by each step, although the deviation between black and white was high since the scope of lesion was big. Upon analysis of optic density divided by appropriate and inappropriate decisions, there were significant differences in concentration difference (D) for fibers, Min (A) and Max (B) concentrations as well as concentration difference (D) for specks, and Max (B) and Mean (C) concentrations with concentration difference (D) for masses.Improvements/Applications: Visual inspections appeared to have difficulty in analyzing lesions due to the ambiguity of quantitative differences. Further developments of quantitative programs are needed to replace visual inspection for breast phantom lesions in mammography.  

scholarly journals An Inexpensive Frame Grabber for the Microscopist

1996 ◽  
Vol 4 (5) ◽  
pp. 16-17
Author(s):  
Richard S. Brown
Keyword(s):  
Digital Image ◽  
Digital Imaging ◽  
Imaging System ◽  
Image Acquisition ◽  
Black And White ◽  
Digital Imaging System ◽  
True Color ◽  
Frame Grabber

Having shopped for an inexpensive but powerful digital imaging system for the last four years, I have finally found a device that will satisfy even the most frugal budget. The Snappy Video Snapshot by Play, Incl, is a 24-bit true color frame grabber that plugs into your PC printer port. After plugging the Snappy device into your printer port (Figure 1) and spending approximately six minutes loading the Snappy software provided, you can connect a color or black and white camera, VCR, or television set and capture your first digital image. Because the device plugs into a printer port, it is completely portable. A switch box is needed if you will be changing between digital image acquisition and printing tasks frequently.

scholarly journals Joint Image Deblurring and Matching with Blurred Invariant-Based Sparse Representation Prior

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yuanjie Shao ◽  
Nong Sang ◽  
Juncai Peng ◽  
Changxin Gao
Keyword(s):  
Sparse Representation ◽  
Image Matching ◽  
Image Deblurring ◽  
Image Feature ◽  
Zernike Moment ◽  
Invariant Space ◽  
Pseudo Zernike Moment ◽  
Image Blurring ◽  
Blurred Image

Image matching is important for vision-based navigation. However, most image matching approaches do not consider the degradation of the real world, such as image blur; thus, the performance of image matching often decreases greatly. Recent methods try to deal with this problem by utilizing a two-stage framework—first resorting to image deblurring and then performing image matching, which is effective but depends heavily on the quality of image deblurring. An emerging way to resolve this dilemma is to perform image deblurring and matching jointly, which utilize sparse representation prior to explore the correlation between deblurring and matching. However, these approaches obtain the sparse representation prior in the original pixel space, which do not adequately consider the influence of image blurring and thus may lead to an inaccurate estimation of sparse representation prior. Fortunately, we can extract the pseudo-Zernike moment with blurred invariant from images and obtain a reliable sparse representation prior in the blurred invariant space. Motivated by the observation, we propose a joint image deblurring and matching method with blurred invariant-based sparse representation prior (JDM-BISR), which obtains the sparse representation prior in the robust blurred invariant space rather than the original pixel space and thus can effectively improve the quality of image deblurring and the accuracy of image matching. Moreover, since the dimension of the pseudo-Zernike moment is much lower than the original image feature, our model can also increase the computational efficiency. Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art blurred image matching approach.

A design of an uncooled thermal imaging system to block image blurring using an optimum detector warm shield

2009 ◽  
Author(s):  
Junho Jeong ◽  
Byungyeol Youn ◽  
Sanghoon Shin ◽  
Sangyeong Park ◽  
Youngjin Kim ◽  
...  
Keyword(s):  
Thermal Imaging ◽  
Imaging System ◽  
Image Blurring ◽  
Thermal Imaging System ◽  
Uncooled Thermal Imaging

A Customizable Thermographic Imaging System for Medical Image Acquisition and Processing

2021 ◽  
pp. 1-1
Author(s):  
Juan A. Lenero-Bardallo ◽  
Rafael De la Rosa-Vidal ◽  
Ruben Padial-Allue ◽  
Joaquin Ceballos-Caceres ◽  
Angel Rodriguez-Vazquez ◽  
...  
Keyword(s):  
Medical Image ◽  
Imaging System ◽  
Image Acquisition ◽  
Thermographic Imaging

scholarly journals Enkripsi Data Citra untuk Model Warna RGB dan Treshold Menggunakan Algoritma Hill Cipher

2019 ◽  
Vol 4 (1) ◽  
pp. 175-178
Author(s):  
Ommi Alfina
Keyword(s):  
Digital Media ◽  
Threshold Model ◽  
Matrix Multiplication ◽  
Digital Data ◽  
Original Form ◽  
Diverse Range ◽  
Black And White ◽  
Hill Cipher ◽  
The Matrix ◽  
The Hill

With the increasingly widespread abuse of digital media, especially in the form of images or images, it increasingly disrupts the rights and privacy of everyone. Many forms of abuse that occur in digital media through internet facilities such as plagiarism of photographer's work, recognition of the rights of the image, to upload photos of privacy to internet media. Therefore, one way to secure digital data in the form of images is to randomize (encrypt) the images that we feel are very important so that the image can no longer be interpreted by others. If we need these data, we just need to return it (decryption) so that the encryption image can return to its original form. Hill Cipher algorithm is one method to randomize a data by encoding and multiplying the matrix. For its application in the form of image data, a trial is needed by creating a software which will then analyze the results into several color models such as RGB, Grayscale (Keabuan) and Tresholding (Black and White). From the test results, it can be concluded that the greater the input matrix value of the Hill Cipher Algorithm, the more encrypted image results will be obtained or in other words the more incomprehensible the visual form by humans. Then the Hill Cipher algorithm cannot be applied to the color threshold model (black and white) because the matrix multiplication obtained does not have a diverse range of values

Restoration of motion blurred image in dual CMOS imaging system

2012 ◽  
Vol 20 (6) ◽  
pp. 1389-1397
Author(s):  
范赐恩 FAN Ci-en ◽  
陈曦 CHEN Xi ◽  
张立国 ZHANG Li-guo ◽  
张虎 ZHANG Hu ◽  
邓德祥 DENG De-xiang
Keyword(s):  
Imaging System ◽  
Blurred Image
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