Ultrasound Detection of Scatterer Concentration by  Weighted Entropy

Tsui, Po-Hsiang

doi:10.3390/e17106598

Cited by 40 publications

(33 citation statements)

References 49 publications

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“…It has been shown that entropy estimated using the probability distribution of ultrasound signals is proportional to ultrasound statistical parameters, and therefore it is expected to inherit the properties of statistical parameters, such as the dependencies of number density of scatterers, attenuation, noise, frequency, transducer focusing, and other factors that affect the size of the resolution cell43. The effects of number density of scatterers on entropy were discussed in the previous paragraph.…”

Section: Discussionmentioning

confidence: 99%

“…For healthy women, dense fibroglandular tissues are more common, and the corresponding density in a breast parenchyma decreases with age48. Ideally, a normal breast parenchyma based on fibroglandular tissues causes a fully developed speckle pattern in the B-mode image, which corresponds to the envelope statistics of the Rayleigh distribution43. Compared with normal breast tissue, benign fibroadenoma is composed of glandular tissues and local fibrous tissues or calcification49.…”

Section: Discussionmentioning

confidence: 99%

“…Two-dimensional (2D) computer simulations were executed at a sampling rate of 50 MHz and a sound speed of 1540 m/s to generate image RF data; previous studies have detailed the simulation method314361. A 5-MHz Gaussian pulse (a pulse length of 0.89 mm, a bandwidth of 80%, and a beam width of 1.66 mm) was generated as the incident wave with a 2D resolution cell with an area of approximately 1.48 mm 2 (0.89 mm × 1.66 mm).…”

Section: Methodsmentioning

confidence: 99%

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Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Tsui

Chen

Kuo

et al. 2017

Sci Rep

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Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Tsui

Chen

Kuo

et al. 2017

Sci Rep

Self Cite

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“…Increasing the scatterer concentration, not only generates a stronger effect of constructive wave interference to cause the echo amplitude distribution to vary toward the Rayleigh distribution [46], but also leads to a larger backscattered amplitude [47,48]. In this condition, various echo amplitudes exist, and the signal uncertainty and unpredictability (entropy) increase [33].…”

Section: Effects Of Fatty Infiltration On Ultrasound Entropymentioning

confidence: 99%

“…From this point of view, non-model-based parameters that can be calculated using any type of data irrespective of the data distribution are suggested as alternatives for characterizing tissues. Among non-model-based statistical parameters, entropy has been shown to correlate with the parameters of the statistical models and the envelope statistics [33], which is a useful clue for characterizing fatty liver [27,28]. Recall that Shannon established information theory and defined entropy as a measure of information uncertainty [34].…”

Section: Introductionmentioning

confidence: 99%

Effects of Fatty Infiltration of the Liver on the Shannon Entropy of Ultrasound Backscattered Signals

Tsui

Wan

2016

Entropy

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This study explored the effects of fatty infiltration on the signal uncertainty of ultrasound backscattered echoes from the liver. Standard ultrasound examinations were performed on 107 volunteers. For each participant, raw ultrasound image data of the right lobe of liver were acquired using a clinical scanner equipped with a 3.5-MHz convex transducer. An algorithmic scheme was proposed for ultrasound B-mode and entropy imaging. Fatty liver stage was evaluated using a sonographic scoring system. Entropy values constructed using the ultrasound radiofrequency (RF) and uncompressed envelope signals (denoted by H R and H E , respectively) as a function of fatty liver stage were analyzed using the Pearson correlation coefficient. Data were expressed as the median and interquartile range (IQR). Receiver operating characteristic (ROC) curve analysis with 95% confidence intervals (CIs) was performed to obtain the area under the ROC curve (AUC). The brightness of the entropy image typically increased as the fatty stage varied from mild to severe. The median value of H R monotonically increased from 4.69 (IQR: 4.60-4.79) to 4.90 (IQR: 4.87-4.92) as the severity of fatty liver increased (r = 0.63, p < 0.0001). Concurrently, the median value of H E increased from 4.80 (IQR: 4.69-4.89) to 5.05 (IQR: 5.02-5.07) (r = 0.69, p < 0.0001). In particular, the AUCs obtained using H E (95% CI) were 0. 93 (0.87-0.99), 0.88 (0.82-0.94), and 0.76 (0.65-0.87) for fatty stages ≥mild, ≥moderate, and ≥severe, respectively. The sensitivity, specificity, and accuracy were 93.33%, 83.11%, and 86.00%, respectively (≥mild). Fatty infiltration increases the uncertainty of backscattered signals from livers. Ultrasound entropy imaging has potential for the routine examination of fatty liver disease.

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Classification of multi‐feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks

Xu,

Zhao,

Wan

et al. 2024

Medical Physics

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BackgroundBreast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI‐RADS) category 4 has the highest false‐positive value of about 30% among five categories. The classification task in BI‐RADS category 4 is challenging and has not been fully studied.PurposeThis work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B‐mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy.MethodsFirst, original US images coming directly from the hospital were cropped and resized. In 1385 B‐mode US BI‐RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K‐means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B‐mode images, K‐means clustering images and entropy images, they are fused in a three‐channel form multi‐feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1‐score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro‐Wilk test. DeLong test and independent t‐test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti‐log compression was conducted but no improvement has shown in CNNs classification results.ResultsWith multi‐feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi‐feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B‐mode images (p < 0.05).ConclusionThe CNNs with multi‐feature fusion show a good potential of reducing the false‐positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false‐positive rate in breast tumor within US BI‐RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.

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Ultrasound Detection of Scatterer Concentration by Weighted Entropy

Cited by 40 publications

References 49 publications

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Effects of Fatty Infiltration of the Liver on the Shannon Entropy of Ultrasound Backscattered Signals

Classification of multi‐feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks

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