Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Shang, Xiaobao; Zhao, Xunwang; Ding, Yong

doi:10.1155/2018/1214697

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…Which is defined as the weighted local quality y i of a region A i with the corresponding local saliency w i . There are many models of saliency discussed with different IQMs to improve prediction performance [30], [32]. Also, some parameters are famous like Dice Similarity Coefficient (DSC), peak signal-to-noise ratio (PSNR) metric, Haussdorff Distance (HD), and probability of tumor detection (PTD).…”

Section: ) the Overall Image Qualitymentioning

confidence: 99%

A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

Geweid

Abdallah

2019

IEEE Access

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Breast cancer identification is the first and foremost step in the journey of proper diagnosis and treatment of this disease. Therefore, many medical examinations and applications are devised and used including different approaches for breast imaging. For medical image analysis, nonparametric algorithm has been used. This work focuses on improving breast cancer recognition in mammogram images using a nonparametric approach based on image pixel intensities (IBCNP). A nonparametric approach is a new expression to deal with uncertainty. In this paper, the nonparametric approach is used to solve the uncertainty in breast cancer detection; uncertainty means no one knows for sure how to detect breast cancer without imaging, which creates an uncertainty for the oncologists. This paper investigates the feasibility of applying the nonparametric approach in mammography (x-ray breast imaging) to detect and hence diagnose and treat breast cancer. Analysing breast images is a very complicated process and requires distinguishing normal and malignant pixel intensities. Maximizing the shared data between image pixel intensities and the desired areas (infected region) is defined as the identification problem, subject to a constraint on the total size of the desired area borders. As part of this research, a comparative study was conducted on the proposed algorithm, Breast Cancer Cells Infestation based on a set of partial differential equations (PDEs), Incoherent Motion in Breast Cancer (IMBC), Global Dynamics of a Breast Cancer Competition Model using five simple differential equations (SDFs), and Estimation of Intensity Using Nonparametric Method (EINP) techniques. These five techniques of medical image segmentation are applied to 156 different mammogram images to obtain the exact measurement of the efficiency of the identification process. This process uses multiple performance metrics such as the overall mammogram image quality (Q), the dice similarity coefficient (DSC), Peak signalto-noise ratio (PSNR) metric, Haussdorff Distance (HD), and probability of tumor detection (PTD). This paper assumes lack of knowledge of the probability densities in relation to pixel intensities of an image within a given area. It proposes an estimation of non-parametric density that formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm was applied to 156 sets of different mammogram images from twelve groups with both malignant and/or normal. The experimental findings showed that the proposed algorithm is 94.937% accurately effective in breast cancer detection. INDEX TERMSMammograms image, pixel intensities, nonparametric technique, breast cancer detection.

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Section: ) the Overall Image Qualitymentioning

confidence: 99%

A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

Geweid

Abdallah

2019

IEEE Access

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“…However, the latest generation of metrics exploit the multi-domain information which simulates well the hierarchical structure of the visual cortex perception [8][9] (e.g. WG-LAB [10] and metrics proposed in [11] and [12]).…”

Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment Using Singular Value Decomposition Based Dominant Eigenvectors for Feature Selection

Sadou¹,

Lahoulou²,

Bouden³

et al. 2019

9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)

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In this paper, a new no-reference image quality assessment (NR-IQA) metric for grey images is proposed using LIVE II image database. The features used are extracted from three well-known NR-IQA objective metrics based on natural scene statistical attributes from three different domains. These metrics may contain redundant, noisy or less informative features which affect the quality score prediction. In order to overcome this drawback, the first step of our work consists in selecting the most relevant image quality features by using Singular Value Decomposition (SVD) based dominant eigenvectors. The second step is performed by employing Relevance Vector Machine (RVM) to learn the mapping between the previously selected features and human opinion scores. Simulations demonstrate that the proposed metric performs very well in terms of correlation and monotonicity.

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“…However, the latest generation of metrics exploits the multi-domain information, which simulates well the hierarchical structure of the visual cortex perception [8][9] (e.g. WG-LAB [10] and metrics proposed in [11] and [12]). In this paper, we develop a new and efficient no-reference image quality analysis NR-IQA metric for grey level images.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2019

SIPIJ

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This paper focuses on no-reference image quality assessment(NR-IQA)metrics. In the literature, a wide range of algorithms are proposed to automatically estimate the perceived quality of visual data. However, most of them are not able to effectively quantify the various degradations and artifacts that the image may undergo. Thus, merging of diverse metrics operating in different information domains is hoped to yield better performances, which is the main theme of the proposed work. In particular, the metric proposed in this paper is based on three well-known NR-IQA objective metrics that depend on natural scene statistical attributes from three different domains to extract a vector of image features. Then, Singular Value Decomposition (SVD) based dominant eigenvectors method is used to select the most relevant image quality attributes. These latter are used as input to Relevance Vector Machine (RVM) to derive the overall quality index. Validation experiments are divided into two groups; in the first group, learning process (training and test phases) is applied on one single image quality database whereas in the second group of simulations, training and test phases are separated on two distinct datasets. Obtained results demonstrate that the proposed metric performs very well in terms of correlation, monotonicity and accuracy in both the two scenarios.

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Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Cited by 5 publications

References 38 publications

A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

Blind Image Quality Assessment Using Singular Value Decomposition Based Dominant Eigenvectors for Feature Selection

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