Texture analysis and classification of ultrasound liver images

Gao, Shuang; Peng, Yungui; Guo, Huiling; Liu, Weifeng; Gao, Tianxin; Xu, Yuanqing; Tang, Xiaoying

doi:10.3233/bme-130922

Cited by 40 publications

(19 citation statements)

References 9 publications

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“…The features x17-x90 are the statistical characters related to the gray scale values of the region of interest; x91-x99 are statistics for examining texture features based on the spatial relationship of pixels [27]; x99-x113 are the gray gradient features [28]; and the remaining features x114-x172 correspond to local binary patterns encoding the texture information [29].…”

Section: Feature Extractionmentioning

confidence: 99%

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

et al. 2019

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Featured Application: Maceral Analysis; Coal Processing.Abstract: An intelligent analytical technique which is able to accurately identify maceral components is highly desired in the fields of mining and geology. However, currently available methods based on fixed-size window neglect the shape information, and thus do not work in identifying maceral composition from one entire photomicrograph. To address these concerns, we propose a novel Maceral Identification strategy based on image Segmentation and Classification (MISC). Considering the complex and heterogeneous nature of coal, a two-level coarse-to-fine clustering method based on K-means is employed to divide microscopic images into a sequence of regions with similar attributes (i.e., binder, vitrinite, liptinite and inertinite). Furthermore, comprehensive features along with random forest are utilized to automatically classify binder and seven types of maceral components, including vitrinite, fusinite, semifusinite, cutinite, sporinite, inertodetrinite and micrinite. Evaluations on 39 microscopic images show that the proposed method achieves the state-of-the-art accuracy of 90.44% and serves as the baseline for future research on maceral analysis. In addition, to support the decisions of petrologists during maceral analysis, we developed a standalone software, which is freely available at https:/github.com/GuyooGu/MISC-Master.

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Section: Feature Extractionmentioning

confidence: 99%

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

et al. 2019

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“…Indeed, fat impedance is greater than that of soft tissues and hence in presence of fat, ultrasound signal will be attenuated. 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.…”

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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“…According to previous studies, radiomics has great potential for the classification of liver fibrosis. Gao et al [14] used texture analysis to classify ultrasound liver images, and the classification accuracies of S0-S4 were 100%, 90%, 70%, 90%, and 100%, respectively. Kayaaltı et al [15] used determine liver fibrosis stage by analyzing some texture features of liver CT images.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis

Xue

Jiang

et al. 2020

Eur Radiol

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Objectives To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading. Methods Totally 466 patients undergoing partial hepatectomy were enrolled, including 401 with chronic hepatitis B and 65 without fibrosis pathologically. All patients received elastography and got liver stiffness measurement (LSM) 2-3 days before surgery. We proposed a deep convolutional neural network by TL to analyze images of gray scale modality (GM) and elastogram modality (EM). The TL process was used for liver fibrosis classification by Inception-V3 network which pretrained on ImageNet. The diagnostic performance of TL and non-TL was compared. The value of single modalities, including GM and EM alone, and multimodalities, including GM + LSM and GM + EM, was evaluated and compared with that of LSM and serological indexes. Receiver operating characteristic curve analysis was performed to calculate the optimal area under the curve (AUC) for classifying fibrosis of S4, ≥ S3, and ≥ S2. Results TL in GM and EM demonstrated higher diagnostic accuracy than non-TL, with significantly higher AUCs (all p < .01). Single-modal GM and EM both performed better than LSM and serum indexes (all p < .001). Multimodal GM + EM was the most accurate prediction model (AUCs are 0.950, 0.932, and 0.930 for classifying S4, ≥ S3, and ≥ S2, respectively) compared with GM + LSM, GM and EM alone, LSM, and biomarkers (all p < .05). Conclusions Liver fibrosis can be staged by a transfer learning modal based on the combination of gray scale and elastogram ultrasound images, with excellent performance. Key Points • Transfer learning consists in applying to a specific deep learning algorithm that pretrained on another relevant problem, expected to reduce the risk of overfitting due to insufficient medical images. • Liver fibrosis can be staged by transfer learning radiomics with excellent performance. • The most accurate prediction model of transfer learning by Inception-V3 network is the combination of gray scale and elastogram ultrasound images.

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Texture analysis and classification of ultrasound liver images

Cited by 40 publications

References 9 publications

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

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

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis

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