Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features

Acharya, U. Rajendra; Raghavendra, U.; Koh, Joel En Wei; Meiburger, Kristen M.; Ciaccio, Edward J.; Hagiwara, Yuki; Molinari, Filippo; Leong, Wai Ling; Vijayananthan, Anushya; Yaakup, Nur Adura; Fabell, Mohd Kamil Bin Mohd; Yeong, Chai Hong

doi:10.1016/j.cmpb.2018.10.006

Cited by 21 publications

(6 citation statements)

References 46 publications

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“…However, some published methods, including the features of HOS, gray-level co-occurrence matrix, wavelet transformation approaches have been approved of regarding their classification performance among normal liver and fatty liver ultrasounds. 13,18…”

Section: Discussionmentioning

confidence: 99%

Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study

Chen

et al. 2019

Proc Inst Mech Eng H

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The B-mode ultrasound usually contains scattering speckle noise which reduces the detailed resolution of the target and is regarded as an intrinsic noise that interferes with diagnostic precision. The aim of this study was to classify hepatic steatosis through applying attenuation correction with a phantom to reduce speckle noise in liver ultrasound tomography in patients. This retrospective study applied three randomized groups signifying different liver statuses. A total of 114 patients’ effective liver ultrasound images—30 normal, 44 fatty, and 40 cancerous—were included. The proposed depth attenuation correction method was first applied to images. Three regions of interest were manually drawn on the images. Next, five feature values for the regions of interest were calculated. Finally, the hybrid method of logistic regression and support vector machine was employed to classify the ultrasound images with 10-fold cross-validation. The accuracy, kappa statistic, and mean absolute error of the proposed hybrid method were 87.5%, 0.812, and 0.119, respectively, which were higher than those of the logistic regression method—75.0%, 0.548, and 0.280—or those of the support vector machine method—75.7%, 0.637, and 0.293—respectively. Therefore, the hybrid method has been proven to be more accurate and have better performance and less error than either single method. The hybrid method provided acceptable accuracy of classification in three liver ultrasound image groups after depth attenuation correction. In the future, the deep learning approaches may be considered for the application in classifying liver ultrasound images.

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Section: Discussionmentioning

confidence: 99%

Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study

Chen

et al. 2019

Proc Inst Mech Eng H

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“…The Hu's moment represents the invariant image patterns. The Hu's moments for an image f(x,y) can be given as [ 38 , 39 ]:

…”

Section: Approach For Covid-19 Detectionmentioning

confidence: 99%

“…The central moments for non negative values of m and n can be defined as [ 39 ]:

where,

and

. Therefore, the normalized central moments can be denoted as:

0123

where,

.…”

Section: Approach For Covid-19 Detectionmentioning

confidence: 99%

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LBP-based information assisted intelligent system for COVID-19 identification

Maheshwari

Sharma

Kumar

2021

Computers in Biology and Medicine

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A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.

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“…Kayaaltı et al [15] used determine liver fibrosis stage by analyzing some texture features of liver CT images. Acharya et al [16] used the kernel discriminant analysis and analysis of variance techniques to classify images into various stages of liver fibrosis. Yeh et al [17] extracted image features from gray level concurrence and non-separable wavelet transform to classify fibrosis with support vector machine.…”

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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Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features

Cited by 21 publications

References 46 publications

Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study

Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study

LBP-based information assisted intelligent system for COVID-19 identification

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

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