Enhancement of ultrasound images using modified anisotropic diffusion model in non-subsampled shearlet domain

Bedi, Anterpreet Kaur; Sunkaria, Ramesh Kumar; Mittal, Deepti

doi:10.1109/ccaa.2017.8229964

Cited by 2 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results support the finding of subjective evaluation and reflects that the template-3 have better candidacy with proposed method than that of template-5. The proposed method with template-3 is further compared with SRAD 3 and MSRAD 11 and recent enhancement techniques, that is, Shearlet-MSRAD 18 and Shahin et al 35 Figure 2 shows clinically acquired ultrasound image and processed images for visual illustration with compared enhancement methods. It can be observed that processed image by SRAD method have fading textural information.…”

Section: Resultsmentioning

confidence: 99%

“…It can be seen clearly that the proposed method further improves the visualization of image textural quality in terms of reduced blurring and better contrast. Furthermore, Figure 3 presents the graphical illustration of all objective performance metrics with proposed method, SRAD, 3 MSRAD, 11 Shearlet-MSRAD, 18 and Shahin et al 35 The PSNR values of proposed method in comparison with other methods show that a limited reduction in speckle. That is supported by the fact that speckle removal has been done up to an extent to reduce blurring not the textural details.…”

Section: Resultsmentioning

confidence: 99%

“…[15][16][17][18] Bedi et al combined the MSRAD with the non-subsampled shearlet transform for despeckling the ultrasound images (termed as Shearlet-MSRAD in this paper). 18 The method involves the decomposition of images using the shearlet transform. Then, shearlet transform thresholding function was applied on the detailed, that is, the high frequency coefficients and MSRAD was applied on the approximate, that is, the low frequency coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…This multidimensional and multidirectional method enhances the visual characteristics of the ultrasound images and remove speckle noise. 18 These methods perform better on edge preservation; however, artifacts often occur. Chandra and Bajpai worked on a linear fractional mesh-free partial differential equation for enhancement of medical images.…”

Section: Introductionmentioning

confidence: 99%

“…Processed and zoomed image of liver. 1: Original image; 2: Processed by SRAD 3 ; 3: Processed by MSRAD 11 ; 4: Processed by Shearlet-MSRAD18 ; 5: Processed by Shahin et al35 ; 6: Processed by proposed method.…”

mentioning

confidence: 99%

See 4 more Smart Citations

An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score

Bharti

Mittal

2020

Ultrason Imaging

Self Cite

View full text Add to dashboard Cite

Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local mean intensity and edge detection. Then, NSS is utilized to extract the enhanced coefficient and this enhanced coefficient is applied to scale the input image. This scaling reflects contrast improvement and denoising effect on ultrasound images. The performance of proposed enhancement method is evaluated on clinical ultrasound images, using both subjective and objective image quality measures. In subjective evaluation, with proposed method, overall best score of 4.3 was obtained and that was 44% higher than the score of original images. These results were also supported by objective measures. The results demonstrated that the proposed method outperformed the other methods in terms of mean brightness preservation, edge preservation, structural similarity, and human perception-based image quality assessment. Thus, the proposed method can be used in computer-aided diagnosis systems and to visually assist radiologists in their interactive-decision-making task.

show abstract

Section: Resultsmentioning

confidence: 99%