Staging the hepatic fibrosis on CT images: Optimizing the slice thickness and texture features

Li, Wendong; Zeng, Yufan; Zhang, Xuejun; Huang, Yu; Fujita, Hiroshi

doi:10.1109/isbb.2011.6107698

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…Comparing with our previous research on the fibrous texture extracted from hepatic CT images [3], Fig. 3 indicate that 3 to 7 fibrous texture features extracted from hepatic MR images are the optimal number of texture combination.…”

Section: Resultsmentioning

confidence: 62%

“…Juan Wang et al [1] have reported that real-time elastography with a new quantitative technology for diffuse histological lesion is a new and promising sonography-based noninvasive method for the assessment of liver fibrosis in patients with chronic hepatitis B. Ahmed M. Hashem et al [2] have showed the result that the area under the receiver operator curve (AUROC) values of the multistage model ranged from 0.874 to 0.974 is a higher range than a single stage classification model with similar conditions. W. Li et al [3] proposed that features 5 to 7 with slice thickness of 1.25mm in classification of fibrosis is the optimal combination on computed tomography images.…”

Section: Introductionmentioning

confidence: 99%

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Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

Zeng

Zhang

Yang

et al. 2013

AMR

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The fibrous texture in liver is one of important signs for interpreting the chronic liver diseases in radiologists’ routines. In order to investigate the usefulness of various texture features calculated by computer algorithm on hepatic magnetic resonance (MR) images, 15 texture features were calculated from the gray level co-occurrence matrix (GLCM) within a region of interest (ROI) which was selected from the MR images with 6 stages of hepatic fibrosis. By different combination of 15 features as input vectors, the classifier had different performance in staging the hepatic fibrosis. Each combination of texture features was tested by Support Vector Machine (SVM) with leave one case out method. 173 patients’ MR images including 6 stages of hepatic fibrosis were scanned within recent two years. The result showed that optimal number of features was confirmed from 3 to 7 by investigating the classified accuracy rate between each stage/group. It is evident that angular second moment, entropy, sum average and sum entropy played the most significant role in classification.

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Section: Resultsmentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

Zeng

Zhang

Yang

et al. 2013

AMR

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“…In the last decade, different types of computer-aided detection/diagnosis (CAD) systems have been developed to ease the workload of radiologists. Some CAD systems for quantitative assessment of liver fibrosis have been shown their promising results by analyzing morphology changes [16,17] and the texture pattern of fibrosis [18] on CT/MR images. Recently, the degree of stiffness has also been analyzed through MR elastography.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Assessment of Hepatic Fibrosis by Elastic Measurement of Liver Using Magnetic Resonance Tagging Images

et al. 2018

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To date, the measurement of the stiffness of liver requires a special vibrational tool that limits its application in many hospitals. In this study, we developed a novel method for automatically assessing the elasticity of the liver without any use of contrast agents or mechanical devices. By calculating the non-rigid deformation of the liver from magnetic resonance (MR) tagging images, the stiffness was quantified as the displacement of grids on the liver image during a forced exhalation cycle. Our methods include two major processes: (1) quantification of the non-rigid deformation as the bending energy (BE) based on the thin-plate spline method in the spatial domain and (2) calculation of the difference in the power spectrum from the tagging images, by using fast Fourier transform in the frequency domain. By considering 34 cases (17 normal and 17 abnormal liver cases), a remarkable difference between the two groups was found by both methods. The elasticity of the liver was finally analyzed by combining the bending energy and power spectral features obtained through MR tagging images. The result showed that only one abnormal case was misclassified in our dataset, which implied our method for non-invasive assessment of liver fibrosis has the potential to reduce the traditional liver biopsy.

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“…As it is well known, the more cirrhosis categories, the more valuable the classification result is, for it can provide a constructive assistance to doctors and facilitate the process for doctors to produce a specific treatment for patient. Therefore, the study of cirrhosis classification is developing form two categories classification of cirrhosis, such as methods provided by Chen et al [ 2 ], Lee et al [ 1 , 3 ], Hui et al [ 4 ], and Li et al [ 5 ], to precisely class three stages (normal stage, early stage, and middle and advanced stage). Thus, a CAD system which can generate more precise stages is an inevitable tendency in the future.…”

Section: Introductionmentioning

confidence: 99%

“…The early CAD systems of cirrhosis mostly apply CT images, such as the methods provided by Li et al [ 5 ] and Chen et al [ 2 , 6 ]. Compared with CT, MRI provides many advantages including high resolution of soft tissue, no radiation, and multiparameters imaging modalities and it is becoming an effective modality in assessing cirrhosis.…”

Section: Introductionmentioning

confidence: 99%

Cirrhosis Classification Based on Texture Classification of Random Features

Liu

Shao

Zheng

et al. 2014

Computational and Mathematical Methods in Medicine

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Accurate staging of hepatic cirrhosis is important in investigating the cause and slowing down the effects of cirrhosis. Computer-aided diagnosis (CAD) can provide doctors with an alternative second opinion and assist them to make a specific treatment with accurate cirrhosis stage. MRI has many advantages, including high resolution for soft tissue, no radiation, and multiparameters imaging modalities. So in this paper, multisequences MRIs, including T1-weighted, T2-weighted, arterial, portal venous, and equilibrium phase, are applied. However, CAD does not meet the clinical needs of cirrhosis and few researchers are concerned with it at present. Cirrhosis is characterized by the presence of widespread fibrosis and regenerative nodules in the hepatic, leading to different texture patterns of different stages. So, extracting texture feature is the primary task. Compared with typical gray level cooccurrence matrix (GLCM) features, texture classification from random features provides an effective way, and we adopt it and propose CCTCRF for triple classification (normal, early, and middle and advanced stage). CCTCRF does not need strong assumptions except the sparse character of image, contains sufficient texture information, includes concise and effective process, and makes case decision with high accuracy. Experimental results also illustrate the satisfying performance and they are also compared with typical NN with GLCM.

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Staging the hepatic fibrosis on CT images: Optimizing the slice thickness and texture features

Cited by 3 publications

References 6 publications

Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

Non-Invasive Assessment of Hepatic Fibrosis by Elastic Measurement of Liver Using Magnetic Resonance Tagging Images

Cirrhosis Classification Based on Texture Classification of Random Features

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