An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score

2020 ◽  
Vol 42 (6) ◽  
pp. 271-283
Author(s):  
Puja Bharti ◽  
Deepti Mittal
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Subjective Evaluation ◽  
Similarity Score ◽  
Input Image ◽  
Ultrasound Images ◽  
Edge Preservation ◽  
Low Contrast ◽  
Interactive Decision Making ◽  
Enhancement Method

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.

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

scholarly journals No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features

2021 ◽  
Vol 7 (7) ◽  
pp. 112
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Multiple Scales ◽  
Digital Images ◽  
Input Image ◽  
Perceptual Quality ◽  
Reference Image ◽  
Benchmark Datasets ◽  
In The Wild

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).

scholarly journals Comparison of low-contrast detectability between uniform and anatomically realistic phantoms—influences on CT image quality assessment

2021 ◽  
Author(s):  
Juliane Conzelmann ◽  
Ulrich Genske ◽  
Arthur Emig ◽  
Michael Scheel ◽  
Bernd Hamm ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Image Quality Assessment ◽  
Lesion Detection ◽  
Detection Accuracy ◽  
Test Environment ◽  
Low Contrast ◽  
Dose Effects

Abstract Objectives To evaluate the effects of anatomical phantom structure on task-based image quality assessment compared with a uniform phantom background. Methods Two neck phantom types of identical shape were investigated: a uniform type containing 10-mm lesions with 4, 9, 18, 30, and 38 HU contrast to the surrounding area and an anatomically realistic type containing lesions of the same size and location with 10, 18, 30, and 38 HU contrast. Phantom images were acquired at two dose levels (CTDIvol of 1.4 and 5.6 mGy) and reconstructed using filtered back projection (FBP) and adaptive iterative dose reduction 3D (AIDR 3D). Detection accuracy was evaluated by seven radiologists in a 4-alternative forced choice experiment. Results Anatomical phantom structure impaired lesion detection at all lesion contrasts (p < 0.01). Detectability in the anatomical phantom at 30 HU contrast was similar to 9 HU contrast in uniform images (91.1% vs. 89.5%). Detection accuracy decreased from 83.6% at 5.6 mGy to 55.4% at 1.4 mGy in uniform FBP images (p < 0.001), whereas AIDR 3D preserved detectability at 1.4 mGy (80.7% vs. 85% at 5.6 mGy, p = 0.375) and was superior to FBP (p < 0.001). In the assessment of anatomical images, superiority of AIDR 3D was not confirmed and dose reduction moderately affected detectability (74.6% vs. 68.2%, p = 0.027 for FBP and 81.1% vs. 73%, p = 0.018 for AIDR 3D). Conclusions A lesion contrast increase from 9 to 30 HU is necessary for similar detectability in anatomical and uniform neck phantom images. Anatomical phantom structure influences task-based assessment of iterative reconstruction and dose effects. Key Points • A lesion contrast increase from 9 to 30 HU is necessary for similar low-contrast detectability in anatomical and uniform neck phantom images. • Phantom background structure influences task-based assessment of iterative reconstruction and dose effects. • Transferability of CT assessment to clinical imaging can be expected to improve as the realism of the test environment increases.

scholarly journals Patient-based low dose cone beam CT acquisition settings for prostate image-guided radiotherapy treatments on a Varian TrueBeam linear accelerator

2020 ◽  
Vol 93 (1115) ◽  
pp. 20200412
Author(s):  
Maria Antonietta Piliero ◽  
Margherita Casiraghi ◽  
Davide Giovanni Bosetti ◽  
Simona Cima ◽  
Letizia Deantonio ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Quality ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Subjective Evaluation ◽  
Cone Beam ◽  
Low Contrast ◽  
Image Guided Radiotherapy ◽  
Image Guided ◽  
Prostate Cancer Radiotherapy

Objective: To evaluate the performance of low dose cone beam CT (CBCT) acquisition protocols for image-guided radiotherapy of prostate cancer. Methods: CBCT images of patients undergoing prostate cancer radiotherapy were acquired with the settings currently used in our department and two low dose settings at 50% and 63% lower exposure. Four experienced radiation oncologists and two radiation therapy technologists graded the images on five image quality characteristics. The scores were analysed through Visual Grading Regression, using the acquisition settings and the patient size as covariates. Results: The low dose acquisition settings have no impact on the image quality for patients with body profile length at hip level below 100 cm. Conclusions: A reduction of about 60% of the dose is feasible for patients with size below 100 cm. The visibility of low contrast features can be compromised if using the low dose acquisition settings for patients with hip size above 100 cm. Advances in knowledge: Low dose CBCT acquisition protocols for the pelvis, based on subjective evaluation of patient images.

scholarly journals Image Enhancement for Inspection of Cable Images Based on Retinex Theory and Fuzzy Enhancement Method in Wavelet Domain

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 570 ◽  
Author(s):  
Xuhui Ye ◽  
Gongping Wu ◽  
Le Huang ◽  
Fei Fan ◽  
Yongxiang Zhang
Keyword(s):  
Image Quality ◽  
Power Transmission ◽  
Low Frequency ◽  
Speckle Noise ◽  
Standard Test ◽  
Low Contrast ◽  
Inspection Robot ◽  
Fuzzy Enhancement ◽  
Enhancement Method ◽  
Frequency Components

Inspection images of power transmission line provide vision interaction for the operator and the environmental perception for the cable inspection robot (CIR). However, inspection images are always contaminated by severe outdoor working conditions such as uneven illumination, low contrast, and speckle noise. Therefore, this paper proposes a novel method based on Retinex and fuzzy enhancement to improve the image quality of the inspection images. A modified multi-scale Retinex (MSR) is proposed to compensate the uneven illumination by processing the low frequency components after wavelet decomposition. Besides, a fuzzy enhancement method is proposed to perfect the edge information and improve contrast by processing the high frequency components. A noise reduction procedure based on soft threshold is used to avoid the noise amplification. Experiments on the self-built standard test dataset show that the algorithm can improve the image quality by 3–4 times. Compared with several other methods, the experimental results demonstrate that the proposed method can obtain better enhancement performance with more homogeneous illumination and higher contrast. Further research will focus on improving the real-time performance and parameter adaptation of the algorithm.

scholarly journals Methods to Increase the Contrast of the Image with Preserving the Visual Quality

2021 ◽  
Vol 6 (2) ◽  
pp. 140-145
Author(s):  
Mykola Maksymiv ◽  
◽  
Taras Rak
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Contrast Enhancement ◽  
Image Quality Assessment ◽  
Visual Quality ◽  
Image Contrast ◽  
Weighted Sum ◽  
Enhancement Method ◽  
Optimal Target ◽  
Statistical Metrics

Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.

scholarly journals A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3954 ◽  
Author(s):  
Qingqing Fu ◽  
Zhengbing Zhang ◽  
Mehmet Celenk ◽  
Aiping Wu
Keyword(s):  
Contrast Enhancement ◽  
Structural Similarity ◽  
Histogram Equalization ◽  
Input Image ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Low Contrast ◽  
Perceptual Evaluation ◽  
Limit Value ◽  
Enhancement Method

Enabled by piezoceramic transducers, ultrasonic logging images often suffer from low contrast and indistinct local details, which makes it difficult to analyze and interpret geologic features in the images. In this work, we propose a novel partially overlapped sub-block histogram-equalization (POSHE)-based optimum clip-limit contrast enhancement (POSHEOC) method to highlight the local details hidden in ultrasonic well logging images obtained through piezoceramic transducers. The proposed algorithm introduces the idea of contrast-limited enhancement to modify the cumulative distribution functions of the POSHE and build a new quality evaluation index considering the effects of the mean gradient and mean structural similarity. The new index is designed to obtain the optimal clip-limit value for histogram equalization of the sub-block. It makes the choice of the optimal clip-limit automatically according to the input image. Experimental results based on visual perceptual evaluation and quantitative measures demonstrate that the proposed method yields better quality in terms of enhancing the contrast, emphasizing the local details while preserving the brightness and restricting the excessive enhancement compared with the other seven histogram equalization-based techniques from the literature. This study provides a feasible and effective method to enhance ultrasonic logging images obtained through piezoceramic transducers and is significant for the interpretation of actual ultrasonic logging data.

scholarly journals Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ruizhe Deng ◽  
Yang Zhao ◽  
Yong Ding
Keyword(s):  
Feature Extraction ◽  
Visual Cortex ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Subjective Evaluation ◽  
Visual Saliency ◽  
Perceptual Quality ◽  
Subjective Rating ◽  
Support Vector

Image quality assessment (IQA) is desired to evaluate the perceptual quality of an image in a manner consistent with subjective rating. Considering the characteristics of hierarchical visual cortex, a novel full reference IQA method is proposed in this paper. Quality-aware features that human visual system is sensitive to are extracted to describe image quality comprehensively. Concretely, log Gabor filters and local tetra patterns are employed to capture spatial frequency and local texture features, which are attractive to the primary and secondary visual cortex, respectively. Moreover, images are enhanced before feature extraction with the assistance of visual saliency maps since visual attention affects human evaluation of image quality. The similarities between the features extracted from distorted image and corresponding reference images are synthesized and mapped into an objective quality score by support vector regression. Experiments conducted on four public IQA databases show that the proposed method outperforms other state-of-the-art methods in terms of both accuracy and robustness; that is, it is highly consistent with subjective evaluation and is robust across different databases.

Research on Enhanced of Mine-Underground Picture

2012 ◽  
Vol 490-495 ◽  
pp. 548-552
Author(s):  
Meng Ling Zhao ◽  
Min Xia Jiang
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Ccd Camera ◽  
Histogram Equalization ◽  
Uniform Illumination ◽  
Improve Image Quality ◽  
Low Contrast ◽  
Enhancement Method ◽  
Field Information ◽  
Time Image

Because of the based on S3C6410 Field information recorder mine- underground non-uniform illumination and mine- underground non-uniform illumination that a large of noise collected and transferred,image is low contrast ,dim and dark. Based on the theory of Donoho's wavelet threshold denoising, several typical wavelet threshold denoising methods are compared.the best denoising effect of peak signal to noise ratio is obtained. The image enhancement method that combination of the adaptive thresholding denoising and histogram equalization is proposed. The experiment result shows that the method has a good denoising performance, which removed the readout noise of CCD Camera,at the same time, image quality is improved .So the wavelet enhancement in image processing of mine- underground can improve image quality.

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