scholarly journals Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yundong Liu ◽  
Xufeng He
Keyword(s):  
Medical Imaging ◽  
Ankle Joint ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Gradient Field ◽  
Osteochondral Injury ◽  
Functional Features ◽  
Image Detail ◽  
Detail Enhancement ◽  
Enhancement Algorithm

Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have allowed medical researchers and clinicians to examine the structural and functional features of the human body, thereby assisting the clinical diagnosis. However, due to the highly controlled imaging environment, the imaging process often creates noise, which seriously affects the analysis of the medical images. In this study, a medical imaging enhancement algorithm is presented for ankle joint talar osteochondral injury. The gradient operator is used to transform the image into the gradient domain, and fuzzy entropy is employed to replace the gradient to determine the diffusion coefficient of the gradient field. The differential operator is used to discretize the image, and a partial differential enhancement model is constructed to achieve image detail enhancement. Three objective evaluation indexes, namely, signal-to-noise ratio (SNR), information entropy (IE), and edge protection index (EPI), were employed to evaluate the image enhancement capability of the proposed algorithm. Experimental results show that the algorithm can better suppress noise while enhancing image details. Compared with the original image, the histogram of the transformed image is more uniform and flat and the gray level is clearer.

FDCT on the Edge of the Image Detail Enhancement Improvement

2014 ◽  
Vol 610 ◽  
pp. 443-448
Author(s):  
Yong Zhang ◽  
Yan Qian
Keyword(s):  
Signal To Noise Ratio ◽  
Curvelet Transform ◽  
Objective Evaluation ◽  
Threshold Method ◽  
Shift Method ◽  
Information Preservation ◽  
Image Edge ◽  
Fast Discrete Curvelet Transform ◽  
Image Detail ◽  
Detail Enhancement

Image edge details contains a rich amount of informations, enhancing edge details is the key of image post-processing. Traditional enhancement methods often lead to edge detail information lost. Fortunately, we find the curvelet transform good performance to reflect the detail information in the edge. In this paper, we add Wrap step to USFFT algorithm based on the Fast Discrete Curvelet Transform (FDCT), and adopt cyclic shift method and Er iteration. At the same time, we adopt adaptive threshold method. In order to get the objective evaluation result, comparing the wavelet algorithm and FDCT to the proposed method, we select peak signal-to-noise ratio. Experimental results show that the proposed method is not only superior to wavelet method, but also superior to single FDCT in the edge and detail information preservation.

Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network

2015 ◽  
Vol 68 ◽  
pp. 10-14 ◽  
Author(s):  
Shaosheng Dai ◽  
Qin Liu ◽  
Pengfei Li ◽  
Jinsong Liu ◽  
Haiyan Xiang
Keyword(s):  
Lateral Inhibition ◽  
Infrared Image ◽  
Image Detail ◽  
Detail Enhancement ◽  
Enhancement Algorithm

Infrared image detail enhancement based on the gradient field specification

2014 ◽  
Vol 53 (19) ◽  
pp. 4141 ◽  
Author(s):  
Wenda Zhao ◽  
Zhijun Xu ◽  
Jian Zhao ◽  
Fan Zhao ◽  
Xizhen Han
Keyword(s):  
Infrared Image ◽  
Gradient Field ◽  
Image Detail ◽  
Detail Enhancement

Color image detail enhancement based on quaternion guided filter

2017 ◽  
Vol 24 (4) ◽  
pp. 40-50 ◽  
Author(s):  
Wu Kun ◽  
Li Guiju ◽  
Han Guangliang ◽  
Yang Hang ◽  
Liu Peixun
Keyword(s):  
Color Image ◽  
Guided Filter ◽  
Image Detail ◽  
Detail Enhancement

scholarly journals Analisis Perbandingan Nilai Signal to Noise Ratio (SNR) pada Pemeriksaan MRI Ankle Joint dengan Menggunakan Quad Knee Coil dan Flex/Multipurpose Coil

2017 ◽  
Vol 3 (1) ◽  
pp. 220-224
Author(s):  
Ahda Nur Arifah ◽  
Yeti Kartikasari ◽  
Emi Murniati
Keyword(s):  
Achilles Tendon ◽  
Ankle Joint ◽  
Subtalar Joint ◽  
Signal To Noise Ratio ◽  
P Value ◽  
Signal To Noise ◽  
Talocrural Joint ◽  
Knee Coil ◽  
Noise Ratio ◽  
Joint Examination

Background : Research on the difference comparison the value of Signal To Noise Ratio (SNR) at MRI Ankle Joint examination using Quad Knee Coil and Flex/Multipurpose Coil at the hospital's radiology installation Telogorejo Semarang. Quad knee coil is a volume coil, is a coil that can act as a transmitter and receiver at the same RF signal (transreceiver). Flex / Multipurpose Coil is a surface coil which has a high SNR for a superficial examination (a small organ). The purpose of this research is to know comparison the value of signal to noise ratio (SNR) and higher the value of signal to noise ratio (SNR) at MRI Ankle Joint examination using Quad Knee Coil and Flex / Multipurpose Coil.Method : This type of research is quantitative experimental approach. The research data which 6 samples. Rate includes images subjectively talocalcaneal interosseous ligament, talocrural joint, subtalar joint, the calcaneus, tibia, talus, and the Achilles tendon. Then the results of the data in Paired T-Test tested.Results : Test results that there are differences in comparison the value of signal to noise ratio (SNR) at MRI Ankle Joint examination using Quad Knee Coil and Flex / Multipurpose Coil which has a p-value / sig for all of 0.002, and each criterion that have talocalcaneal interoseous ligament p value 0.026, talocrural joint p value 0.017, subtalar joint p value 0.001, calcaneus p value 0.002, tibia p value 0.003, talus p value 0.006, and achilles tendon p value 0.012. This is in accordance with the calculated average value SNR on the use of Quad Knee Coil is higher at 110.67 because the coil acts as transreceiver and has two preamplifier so as to improve the SNRConclusion : There is a differences in comparison the value of Signal To Noise Ratio (SNR) at MRI Ankle Joint examination using Quad Knee Coil and Flex / Multipurpose Coil.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

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