Ultrasound Entropy Imaging of Nonalcoholic Fatty Liver Disease: Association with Metabolic Syndrome

Lin, Ying-Hsiu; Liao, Yin-Yin; Yeh, Chih‐Kuang; Yang, Kuen-Cheh; Tsui, Po-Hsiang

doi:10.3390/e20120893

Cited by 29 publications

(24 citation statements)

References 60 publications

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“…Zhou et al [22] demonstrated that entropy imaging yields a better performance than Nakagami imaging, for both liver donors and patients. Lin et al [60] compared entropy imaging with ASQ imaging and showed that entropy imaging outperforms ASQ imaging. More comparisons between different ultrasound envelope statistics parametric imaging techniques are expected in future developments.…”

Section: Discussionmentioning

confidence: 99%

“…A larger value of Shannon entropy indicates a greater uncertainty, representing that the ultrasound backscattered signals change from regular to random and even complex states [58]. Clinical studies demonstrated that Shannon entropy increases with increasing severity of hepatic steatosis [22,59,60]. Tsui et al [59] investigated ultrasound entropy imaging for assessing hepatic steatosis of 107 patients, using an ultrasonographic scoring system [36] as the reference standard.…”

Section: Ultrasound Entropy Imagingmentioning

confidence: 99%

“…A correlation r = 0.63 (p < 0.0001) was reported. Lin et al [60] assessed hepatic steatosis of 394 patients with ultrasound entropy and ASQ imaging; the reference standard was an ultrasonographic fatty liver indicator [61]. Correlations of r = 0.713 (p < 0.0001) and r = -0.630 (p < 0.0001) were reported for entropy and ASQ imaging, respectively.…”

Section: Ultrasound Entropy Imagingmentioning

confidence: 99%

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Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Zhou

Zhang

et al. 2019

Applied Sciences

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Hepatic steatosis is a key manifestation of non-alcoholic fatty liver disease (NAFLD). Early detection of hepatic steatosis is of critical importance. Currently, liver biopsy is the clinical golden standard for hepatic steatosis assessment. However, liver biopsy is invasive and associated with sampling errors. Ultrasound has been recommended as a first-line diagnostic test for the management of NAFLD. However, B-mode ultrasound is qualitative and can be affected by factors including image post-processing parameters. Quantitative ultrasound (QUS) aims to extract quantified acoustic parameters from the ultrasound backscattered signals for ultrasound tissue characterization and can be a complement to conventional B-mode ultrasound. QUS envelope statistics techniques, both statistical model-based and non-model-based, have shown potential for hepatic steatosis characterization. However, a state-of-the-art review of hepatic steatosis assessment using envelope statistics techniques is still lacking. In this paper, envelope statistics-based QUS parametric imaging techniques for characterizing hepatic steatosis are reviewed and discussed. The reviewed ultrasound envelope statistics parametric imaging techniques include acoustic structure quantification imaging, ultrasound Nakagami imaging, homodyned-K imaging, kurtosis imaging, and entropy imaging. Future developments are suggested.

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Section: Discussionmentioning

confidence: 99%

Section: Ultrasound Entropy Imagingmentioning

confidence: 99%

Section: Ultrasound Entropy Imagingmentioning

confidence: 99%

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Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Zhou

Zhang

et al. 2019

Applied Sciences

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“…In the case of low-quality images, numerous image processing techniques utilise information entropy as a tool for image feature extraction [ 1 ]. For instance, entropy is of great use in studying and examining the liver and other soft tissue organs [ 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

Hržić

Štajduhar

Tschauner

et al. 2019

Entropy

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The paper proposes a segmentation and classification technique for fracture detection in X-ray images. This novel rotation-invariant method introduces the concept of local entropy for de-noising and removing tissue from the analysed X-ray images, followed by an improved procedure for image segmentation and the detection of regions of interest. The proposed local Shannon entropy was calculated for each image pixel using a sliding 2D window. An initial image segmentation was performed on the entropy representation of the original image. Next, a graph theory-based technique was implemented for the purpose of removing false bone contours and improving the edge detection of long bones. Finally, the paper introduces a classification and localisation procedure for fracture detection by tracking the difference between the extracted contour and the estimation of an ideal healthy one. The proposed hybrid method excels at detecting small fractures (which are hard to detect visually by a radiologist) in the ulna and radius bones—common injuries in children. Therefore, it is imperative that a radiologist inspecting the X-ray image receives a warning from the computerised X-ray analysis system, in order to prevent false-negative diagnoses. The proposed method was applied to a data-set containing 860 X-ray images of child radius and ulna bones (642 fracture-free images and 218 images containing fractures). The obtained results showed the efficiency and robustness of the proposed approach, in terms of segmentation quality and classification accuracy and precision (up to 91.16 % and 86.22 % , respectively).

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“…Entropies are often used for image analysis [ 19 , 35 , 36 ]. There are two approaches for determining the structural entropy.…”

Section: Introductionmentioning

confidence: 99%

On Structural Entropy and Spatial Filling Factor Analysis of Colonoscopy Pictures

Nagy

Sziová

Pipek

2019

Entropy

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Colonoscopy is the standard device for diagnosing colorectal cancer, which develops from little lesions on the bowel wall called polyps. The Rényi entropies-based structural entropy and spatial filling factor are two scale- and resolution-independent quantities that characterize the shape of a probability distribution with the help of characteristic curves of the structural entropy–spatial filling factor map. This alternative definition of structural entropy is easy to calculate, independent of the image resolution, and does not require the calculation of neighbor statistics, unlike the other graph-based structural entropies.The distant goal of this study was to help computer aided diagnosis in finding colorectal polyps by making the Rényi entropy based structural entropy more understood. The direct goal was to determine characteristic curves that can differentiate between polyps and other structure on the picture. After analyzing the distribution of colonoscopy picture color channels, the typical structures were modeled with simple geometrical functions and the structural entropy–spatial filling factor characteristic curves were determined for these model structures for various parameter sets. A colonoscopy image analying method, i.e., the line- or column-wise scanning of the picture, was also tested, with satisfactory matching of the characteristic curve and the image.

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Ultrasound Entropy Imaging of Nonalcoholic Fatty Liver Disease: Association with Metabolic Syndrome

Cited by 29 publications

References 60 publications

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

On Structural Entropy and Spatial Filling Factor Analysis of Colonoscopy Pictures

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