scholarly journals Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Zhou ◽  
ZhenHong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Image Enhancement ◽  
Fuzzy Set ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Low Frequency ◽  
Noise Removal ◽  
Adaptive Threshold ◽  
Image Noise ◽  
Medical Image Enhancement

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect.

A Novel Medical Image Enhancement Algorithm Based on Dyadic Wavelet Transform

2014 ◽  
Vol 513-517 ◽  
pp. 2726-2729 ◽  
Author(s):  
Jia Li ◽  
Yun Feng Yang ◽  
Peng Xiao Wang ◽  
Bo Li
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Low Contrast ◽  
Frequency Information ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Medical Image Enhancement

In clinical application, medical images tend to be low contrast, bigger or more speckle noise, and they will affect the effective use of medical images. Medical image enhancement can solve the problem of the low contrast of the image, so as to get more clear details of images. An algorithm of the medical image enhancement is proposed based on the binary wavelet transform in the paper. Firstly, the medical image was carried through dyadic wavelet transform, then the high-frequency information was de-noised, and then to enhance the high frequency information which was de-noised; at last, the enhanced high-frequency sub-images and the low-frequency sub-images were reconstructed by inverse dyadic wavelet transform. Finally, a better visual effect can be got by a subsection grayscale transform. The experiment results show the enhanced effect of proposed method is better than those of the wavelet transform.

scholarly journals Gradient, Texture Driven Based Dynamic-Histogram Equalization For Medical Image Enhancement

2021 ◽  
Vol 15 ◽  
pp. 303-310
Author(s):  
H. N. Vidyasaraswathi ◽  
M. C. Hanumantharaju
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Images ◽  
Similarity Index ◽  
Mapping Function ◽  
Structural Similarity ◽  
Histogram Equalization ◽  
Visual Appearance ◽  
Index Measurement ◽  
Medical Image Enhancement

In many clinical diagnostic measurements, medical images play some significant role but often suffer from various types of noise and low-luminance, which causes some notable changes in overall system accuracy with misdiagnosis rate. To improve the visual appearance of object regions in medical images, image enhancement techniques are used as potential pre-processing techniques. Due to its simplicity and easiness of implementation, histogram equalization is widely preferred in many applications. But due to its mapping function based image transformation during enhancement process affect the biomedical patterns which are essential for diagnosis. To mitigate these issues in medical images, a new method based on gradient computations and Texture Driven based Dynamic histogram equalization (GTDDHE) is accomplished to increase the visual perception. The spatial texture pattern is also included to ensure the texture retention and associated control over its variations during histogram modifications. Experimental results on MRI, CT images, eyes images from medical image datasets and quantitative analysis by PSNR, structural similarity index measurement (SSIM), information entropy (IE) and validated that the proposed method offers improved quality with maximum retention of biomedical patterns across all types of medical images.

Wavelet-Based Medical Image Denoising and Enhancement

2012 ◽  
Vol 195-196 ◽  
pp. 515-520
Author(s):  
Hui Qin Jiang ◽  
Zhong Yong Wang ◽  
Ling Ma ◽  
Yu Min Liu ◽  
Ping Li
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Dynamic Range ◽  
Medical Images ◽  
Frequency Component ◽  
Noise Removal ◽  
High Frequency Component ◽  
High Signal ◽  
Noise Component

The visual quality of medical images is an important aspect in PACS implementation. In this study, on the basis of wavelet analysis, a denoising and enhancement algorithm for medical image is proposed. The algorithm mainly includes six steps. At first, an effcient method is investigated for Poisson Noise remove. Second, diagnosis features of the denoised image are enhanced by compressing the dynamic range. Third, we extract the high frequency component of the original image by the designed lowpass filter. Fourth, the extracted high frequency component are segment into diagnosis feature component in the high signal range, the diagnosis feature component in the low signal range, and the noise component. Five, we reconstruct an image using image fusion. Finally, we make DICOM calibration for used display and decide parameters of the image fusion, resulting in the diagnosis image. Experimental results show that this new scheme offers effective noise removal in medical images and enhancing sharpness. More importantly, this scheme can improve the diagnostic value of the display image on the commercial display successfully.

Multi-Modal Medical Image Fusion Algorithm Based On Spatial Frequency Motivated PA-PCNN In NSST Domain

2020 ◽  
Vol 16 ◽  
Author(s):  
Vanitha Kamarthi ◽  
D. Satyanarayana ◽  
M.N. Giri Prasad
Keyword(s):  
Mutual Information ◽  
Spatial Frequency ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Low Frequency ◽  
Fusion Algorithm ◽  
Frequency Bands ◽  
Edge Strength

Background: Image fusion has been grown as an effectual method in diseases related diagnosis schemes. Methods: In this paper, a new method for combining multimodal medical images using spatial frequency motivated parameter-adaptive PCNN (SF-PAPCNN) is suggested. The multi-modal images are disintegrated into frequency bands by using decomposition NSST. The coefficients of low frequency bands are selected using maximum rule. The coefficients of high frequency bands are combined by SF-PAPCNN. Results: The fused medical images is obtained by applying INSST to above coefficients. Conclusion: The quality metrics such as entropy ENT, fusion symmetry FS, deviation STD, mutual information QMI and edge strength QAB/F are used to validate the efficacy of suggested scheme.

scholarly journals Image Enhancement using Wavelet Fusion for Medical Image Processing

2019 ◽  
Vol 9 (1) ◽  
pp. 2663-2667
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Images ◽  
Discrete Wavelet ◽  
Low Contrast ◽  
Adaptive Fusion ◽  
Improved Performance ◽  
Medical Image Enhancement ◽  
Contrast Image

Medical Image Enhancement Low contrast is the active study area that the obtained pictures suffer from noise and low contrast. Age of capturing equipment, bad illumination circumstances are the low contrast medical images. Thus, techniques of contrast improved performance are used before being used to enhance the contrast of medical images. Within a tiny range of pixel concentrations, contrast improvement algorithms enhance low contrast image. Low contrast image enhancement is accomplished using Equalization of Contrast Limited Adaptive Histogram. CLAHE image enhancement is used to enhance the quality of medical images with low contrast. DWT image, sub-bands such as LL, LH, HL, HH are decomposed. 2D Adaptive fusion image on discrete wavelet transformation is used to fuse the main and CLAHE output images. The efficiency of the output is calculated using merged image entropy and PSNR. It is discovered that the visual content of low contrast medical pictures is enhanced effectively on the basis of 2D DWT and adaptive Fusion.

scholarly journals Research on an Improved Medical Image Enhancement Algorithm Based on P-M Model

2015 ◽  
Vol 9 (1) ◽  
pp. 209-213 ◽  
Author(s):  
Luo Aijing ◽  
Yin Jin
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Images ◽  
Image Contrast ◽  
Image Brightness ◽  
Medical Image Enhancement ◽  
Enhancement Algorithm

Image enhancement can improve the detail of the image to achieve the purpose of the identification of the image. At present, the image enhancement is widely used in medical images, which can help doctor’s diagnosis. IEABPM (Image Enhancement Algorithm Based on P-M Model) is one of the most common image enhancement algorithms. However, it may cause the loss of the texture details and other features. To solve the problems, this paper proposes an IIEABPM (Improved Image Enhancement Algorithm Based on P-M Model). The simulation demonstrates that IIEABPM can effectively solve the problems of IEABPM, and improve image clarity, image contrast, and image brightness.

scholarly journals Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ling Tan ◽  
Xin Yu
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Medical Image ◽  
Information Transfer ◽  
Medical Images ◽  
Low Frequency ◽  
Source Image ◽  
Fusion Algorithm ◽  
Shearlet Transform

Clinical diagnosis has high requirements for the visual effect of medical images. To obtain rich detail features and clear edges for fusion medical images, an image fusion algorithm FFST-SR-PCNN based on fast finite shearlet transform (FFST) and sparse representation is proposed, aiming at the problem of poor clarity of edge details that is conducive to maintaining the details of source image in current algorithms. Firstly, the source image is decomposed into low-frequency coefficients and high-frequency coefficients by FFST. Secondly, the K-SVD method is used to train the low-frequency coefficients to obtain the overcomplete dictionary D, and then the OMP algorithm sparsely encodes the low-frequency coefficients to complete the fusion of the low-frequency coefficients. Then, a high-frequency coefficient is applied to excite a pulse-coupled neural network, and the fusion coefficient of the high-frequency coefficient is selected according to the number of ignitions. Finally, the fused low-frequency coefficient and high-frequency coefficient are reconstructed into the fused medical image by FFST inverse transform. The experimental results show that the image fusion result of the proposed algorithm is about 35% higher than the comparison algorithms for the edge information transfer factor QAB/F index and has achieved good results in both subjective visual effects and objective evaluation indicators.

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