Effect of ultrasound frequency on the Nakagami statistics of human liver tissues

Tsui, Po-Hsiang; Zhou, Zhuhuang; Lin, Ying; Hung, Chieh Ming; Chung, Shih Jou; Wan, Yung Liang

doi:10.1371/journal.pone.0181789

Cited by 34 publications

(25 citation statements)

References 81 publications

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“…Moreover, in this study we examined only the impact of compression threshold level on classification, but other factors exist that can decrease the performance. For example, the texture of the image might depend on applied beamforming technique [22] or imaging frequency [23]. The proposed method of data augmentation method is general and can be applied to all kinds of US images to improve the CADx system performance with deep learning.…”

Section: Discussionmentioning

confidence: 99%

Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning

Byra

Sznajder

Koržinek

et al. 2019

Lecture Notes in Computer Science

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Deep learning algorithms, especially convolutional neural networks, have become a methodology of choice in medical image analysis. However, recent studies in computer vision show that even a small modification of input image intensities may cause a deep learning model to classify the image differently. In medical imaging, the distribution of image intensities is related to applied image reconstruction algorithm. In this paper we investigate the impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning. Due to high dynamic range raw ultrasonic signals are commonly compressed in order to reconstruct B-mode images. Based on raw data acquired from breast lesions, we reconstruct B-mode images using different compression levels. Next, transfer learning is applied for classification. Differently reconstructed images are employed for training and evaluation. We show that the modification of the reconstruction algorithm leads to decrease of classification performance. As a remedy, we propose a method of data augmentation. We show that the augmentation of the training set with differently reconstructed B-mode images leads to a more robust and efficient classification. Our study suggests that it is important to take into account image reconstruction algorithms implemented in medical scanners during development of computer aided diagnosis systems.

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Section: Discussionmentioning

confidence: 99%

Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning

Byra

Sznajder

Koržinek

et al. 2019

Lecture Notes in Computer Science

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“…The echogenic shadows of the rabbit's liver reflects the histological features of the liver and biliary ducts with variable US acoustics at the same intensity of the US wave in contrast to the ultrasound image of human's liver in which connective tissue is the only morphologic substrate that decides the acoustic properties of the ultrasound appearance (33). The acoustics image of fibrous capsule and hepatic parenchyma in both human and rabbit is related to some ultrasound indices as brightness and contrast according to the grey-white scale, variety of the grey shadow and the speed of the US wave (34).…”

Section: Discussionmentioning

confidence: 99%

Comparative anatomical, histological, and histochemical study of liver in human and domestic rabbit

Al-Hamdany¹

2019

IJVS

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This study was conducted to compare the anatomical histological and histochemical structure of human liver with the liver of domestic rabbits to evaluate the quality of liver of rabbit to be used as a model of mammalian species in the experimental researches. Ten liver biopsies of human (Group I) and same number of liver specimens of domestic rabbit (Group II) were used in this study. The liver specimens were processed and stained with Harris Hematoxylin and Eosin stain and Masson's Trichrome stain and examined under light microscope. For histochemical examination, Periodic Acid Schiff's, Best's carmine and Gomori's Alkaline Phosphatase stains were used. Histomorphometric measurements were done to compare some parameters between the two groups. Transabdominal ultrasound examination was done to compare ultrasonic appearance of the liver in both groups. The work was done from May 2018 to December 2018. The study revealed more lobulated liver of domestic rabbits, hexagonal lobules are mingled with each other in rabbit's liver compared to human, larger hepatocytes, with higher percentage of binucleated hepatocytes and thicker capsule in the human's liver than in the rabbit's liver. Histochemical results revealed less carbohydrates, less glycogen content with less activity of alkaline phosphatase in the cytoplasm of hepatocytes of the human's liver. The ultrasound image revealed denser hyperechoic diaphragm and fibrous capsule, nonhomogenous low echogenicity of parenchyma of the liver of human compared to the rabbit's liver. We conclude that the basic anatomical and histological structure is more or less similar in the human's liver and liver of domestic rabbit but the few differences might be related to the evolutional variations among mammals thus the value of the liver tissue of domestic rabbit to be used in the experimental researches is significant.

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“…When the resolution cell of the transducer contains a large number of randomly distributed scatterers, the statistical distribution of ultrasound backscattered envelopes exhibits the Rayleigh distribution [ 13 , 14 , 15 ]. This condition represents that no macrostructures exist in the tissue to generate information of r

, and thus the FD ratio is theoretically equal to zero.…”

Section: Methodsmentioning

confidence: 99%

“…Quantitative analysis of ultrasound images may provide additional clues to improve the diagnosis of mild NAFLD. Essentially, liver parenchyma can be modeled as a scattering medium consisting of numerous acoustic scatterers [ 13 , 14 ] that interact with the incident wave to form ultrasound backscattered signals. Different scatterer properties result in different waveforms of backscattered signals, and thus the corresponding statistical properties may depend on information associated with changes in liver microstructures [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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

Lin

Liao

Yeh

et al. 2018

Entropy

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Nonalcoholic fatty liver disease (NAFLD) is the leading cause of advanced liver diseases. Fat accumulation in the liver changes the hepatic microstructure and the corresponding statistics of ultrasound backscattered signals. Acoustic structure quantification (ASQ) is a typical model-based method for analyzing backscattered statistics. Shannon entropy, initially proposed in information theory, has been demonstrated as a more flexible solution for imaging and describing backscattered statistics without considering data distribution. NAFLD is a hepatic manifestation of metabolic syndrome (MetS). Therefore, we investigated the association between ultrasound entropy imaging of NAFLD and MetS for comparison with that obtained from ASQ. A total of 394 participants were recruited to undergo physical examinations and blood tests to diagnose MetS. Then, abdominal ultrasound screening of the liver was performed to calculate the ultrasonographic fatty liver indicator (US-FLI) as a measure of NAFLD severity. The ASQ analysis and ultrasound entropy parametric imaging were further constructed using the raw image data to calculate the focal disturbance (FD) ratio and entropy value, respectively. Tertiles were used to split the data of the FD ratio and entropy into three groups for statistical analysis. The correlation coefficient r, probability value p, and odds ratio (OR) were calculated. With an increase in the US-FLI, the entropy value increased (r = 0.713; p < 0.0001) and the FD ratio decreased (r = -0.630; p < 0.0001). In addition, the entropy value and FD ratio correlated with metabolic indices (p < 0.0001). After adjustment for confounding factors, entropy imaging (OR = 7.91, 95% confidence interval (CI): 0.96-65.18 for the second tertile; OR = 20.47, 95% CI: 2.48-168.67 for the third tertile; p = 0.0021) still provided a more significant link to the risk of MetS than did the FD ratio obtained from ASQ (OR = 0.55, 95% CI: 0.27-1.14 for the second tertile; OR = 0.42, 95% CI: 0.15-1.17 for the third tertile; p = 0.13). Thus, ultrasound entropy imaging can provide information on hepatic steatosis. In particular, ultrasound entropy imaging can describe the risk of MetS for individuals with NAFLD and is superior to the conventional ASQ technique.

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Effect of ultrasound frequency on the Nakagami statistics of human liver tissues

Cited by 34 publications

References 81 publications

Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning

Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning

Comparative anatomical, histological, and histochemical study of liver in human and domestic rabbit

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

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