B-scan Image Feature Extraction of Fatty Liver

Daxi, Wang; Fang, Yuan; Hu, Bo; Cao, Hanqiang

doi:10.1109/icicse.2012.61

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Currently, measurements of the mean value of ultrasound attenuation coefficient and texture features were used to distinguish fatty liver from normal liver [1][2][3][4][5][6]. Among them, the most effective available method-gray level co-occurrence matrix [7] was compared with LSM. From the result, this method can be used to diagnose fatty live by feature classification and can be used to extract the information about the location of fat, but it can't be used to evaluate fatty proportion quantitatively.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of fatty proportion in fatty liver using least squares method with constraints

Deng

et al. 2014

Bio-Medical Materials and Engineering

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Backscatter and attenuation parameters are not easily measured in clinical applications due to tissue inhomogeneity in the region of interest (ROI). A least squares method(LSM) that fits the echo signal power spectra from a ROI to a 3parameter tissue model was used to get attenuation coefficient imaging in fatty liver. Since fat's attenuation value is higher than normal liver parenchyma, a reasonable threshold was chosen to evaluate the fatty proportion in fatty liver. Experimental results using clinical data of fatty liver illustrate that the least squares method can get accurate attenuation estimates. It is proved that the attenuation values have a positive correlation with the fatty proportion, which can be used to evaluate the syndrome of fatty liver.

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Section: Introductionmentioning

confidence: 99%

Evaluation of fatty proportion in fatty liver using least squares method with constraints

Deng

et al. 2014

Bio-Medical Materials and Engineering

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“…Figure 2 exhibits a sample image, alongside the outcomes post-preprocessing. For the Feature extraction is an essential step in any pattern recognition task and particularly important for classifying NAFL disease in ultrasound images, due to the low quality of the images and the variability of fatty liver grades [20]. This study utilized seven popular pretrained CNN models trained on the ImageNet dataset to extract features: VGG19, Mo-bileNet, Xception, Inception V3, ResNet-101, DenseNet-121, and EfficientNet-B7.…”

Section: Preprocessingmentioning

confidence: 99%

Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images

2024

J Biomed Phys Eng

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Background: Nonalcoholic Fatty Liver Disease (NAFLD) as a prevalent condition can significantly have health implications. Early detection and accurate grading of NAFLD are essential for effective management and treatment of the disease.Objective: The current study aimed to develop an advanced hybrid machinelearning model to classify NAFLD grades using ultrasound images. Material and Methods:In this analytical study, ultrasound images were obtained from 55 highly obese individuals, who had undergone bariatric surgery and used histological results from liver biopsies as a reference for NAFLD grading. The features were extracted from the ultrasound images using popular pretrained Convolutional Neural Network (CNN) models, including VGG19, MobileNet, Xception, Inception-V3, ResNet-101, DenseNet-121, and EfficientNet-B7. The fully connected layers were removed from the CNN models and also used the remaining structure as a feature extractor. The most relevant features were then selected using the minimum Redundancy Maximum Relevance (mRMR) method. We then used four classification algorithms: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Multilayer Perceptron (MLP) neural network, and Random Forest (RF) classifiers, to categorize the ultrasound images into four groups based on liver fat level (healthy liver, low fat liver, moderate fat liver, and high-fat liver).Results: Among the different CNN models and classification methods, Efficient-Net-B7 and RF achieved the highest accuracy. The average accuracies of the LDA, MLP, SVM, and RF classifiers for the feature extraction method with EfficientNet-B7 were 88.48%, 93.15%, 95.47%, and 96.83%, respectively. The proposed automatic model can classify NAFLD grades with a remarkable accuracy of 96.83%. Conclusion:The proposed automatic classification model using EfficientNet-B7 for feature extraction and a Random Forest classifier can improve NAFLD diagnosis, especially in regions, in which access to professional and experienced medical experts is limited.

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“…Feature Extraction and Selection: In this step, features are extracted and a subset of features is selected to build an optimal set of features that can accurately distinguish diffuse liver diseases. 72,73 The most relevant features for ultrasound images of liver are listed in Table 3. 4.…”

Section: Image Preprocessingmentioning

confidence: 99%

Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging

2016

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Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.

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B-scan Image Feature Extraction of Fatty Liver

Cited by 6 publications

References 4 publications

Evaluation of fatty proportion in fatty liver using least squares method with constraints

Evaluation of fatty proportion in fatty liver using least squares method with constraints

Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images

Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging

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