Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

Zeng, Yu; Zhang, Xue Jun; Yang, Wen; Huang, Yu; Shi, Jia Xiong; Liang, Tian; Huang, Yu

doi:10.4028/www.scientific.net/amr.647.325

Cited by 4 publications

(7 citation statements)

References 5 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We did three stages (normal stage, early stage, and middle and advanced stage) classification experiment with 14 kinds of GLCM texture features of ROIs. And ROIs were extracted from MRI with the same extraction principle which is excluding the diffuse distribution of liver, large blood vessels within the liver [ 8 ]. We perform tenfold cross validation method to execute the classification and the result is shown in Figure 5 .…”

Section: Experiments Resultsmentioning

confidence: 99%

“…The images used for segment are manually selected according to the requirements that the images need to contain a clear and relatively whole liver. ROIs with size of n × n are extracted from five sequences MRIs, and considering the extraction principle of the diffuse distribution of liver, large blood vessels within the liver are excluded [ 8 ]. In this paper, the size of ROI is 30 × 30 or 60 × 60, and it depends on the range in liver that can be extracted.…”

Section: Methodsmentioning

confidence: 99%

“…Actually, effective texture feature that can fully describe cirrhosis feature is a key problem of CAD. GLCM based feature is a classical method to extract texture features for cirrhosis classification [ 4 , 8 – 11 ]. However, GLCM has many weaknesses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cirrhosis Classification Based on Texture Classification of Random Features

Liu

Shao

Zheng

et al. 2014

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

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.

show abstract

Section: Experiments Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Cirrhosis Classification Based on Texture Classification of Random Features

Liu

Shao

Zheng

et al. 2014

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…[12][13][14] The overall performance calculated by the average sum of maximum AR value of all types number of features is 85.74% by shape features, while 66.83% by texture and 75% by volume. The result implies the efficiency of shape features to the diagnosis of liver fibrosis.…”

Section: Feature Importance Analysismentioning

confidence: 99%

Selection of Optimal Shape Features for Staging Hepatic Fibrosis on CT Image

Zhang¹,

Hiroshi²,

Kevin³

et al. 2015

j med imaging hlth inform

View full text Add to dashboard Cite

Computer-aided diagnosis of hepatic fibrosis is playing an increasing role in clinical visits, while the study based on shape features diagnosis is still in the exploratory stage. In order to improve efficiency of interpretation of medical images and diagnostic accuracy, a novel method for informatics feature selection from hepatic shape is proposed in this paper. First, the contour profile of liver is extracted from contrast-enhanced CT images. Then a difference edge curve is obtained from substation of the profile with the polynomial fitting curve, from which ten surface shape features are calculated for the input parameters of the SVM classifier. Finally, feature selection algorithm with cross-validation leave-one-out method is applied to check each combination of all features to provide the accurate rate of staging the fibrosis degree. The result shows that the optimal number of features ranges from two to six among all ten features; statistical analysis shows that maximum roughness depth (R max ), maximum profile valley depth (R p ), maximum profile peak height (R m ), mean spacing of the profile irregularities (S m ) and mean spacing of local peaks of the profile (S have the greater weight than the other features. The experimental result indicates that the accuracy rate of shape feature is considerably higher than other types of features.

show abstract

“…A method presented by Sela et al automatically creates a grading fibrosis model using a hierarchical multi-class binary-based support vector machine (SVM) classifier by f-MRI of rat livers, and can achieve 96.9% and 72.5% classification accuracy for F0 versus F1-5 and F1-3 versus F4-5, respectively [14]. Zeng et al reported a classification accuracy of 85.0%, 66.0%, and 70.0%, for F0-2 versus F5, F0 versus F1-2 and F1-2 versus F3-4, respectively when a SVM classifier based analysis of texture feature in the gadolinium-enhanced portal phase images was performed [15].…”

Section: Introductionmentioning

confidence: 99%

Staging liver fibrosis by analysis of non-linear normalization texture in gadolinium-enhanced magnetic resonance imaging

Mou

Guo

Liu

et al. 2015

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

A reliable non-invasive approach for accurate staging fibrosis is still under development. In this study, we utilized a computer aided diagnosis (CAD) system to analyze equilibrium phase magnetic resonance (MR) images in a rat model to determine relative accuracy of staging liver fibrosis with CAD. Methods: Experimental rats were injected with a mixture of CCl 4 to generate varying stages of fibrosis and equilibrium phase images of rats were acquired. All rats were grouped based on histological stages (F0, F1, F2, F3 and F4). During the process of CAD, the first step was manual section of region of interest (ROI) with 10 * 10 pixels guided by histology. Next, Lloyd's algorithm and linear normalization were both used to compress ROIs to 256 gray level. Then, 80 texture features based on the gray level co-occurrence matrix were extracted. Lastly, the back-propagation (BP), linear, k-nearest neighbor and support vector machine classifiers were individually used to classify F0-F4.Results: With histological staging as a reference, we found that performance of the BP classifier based on Lloyd's algorithm in staging fibrosis was better than that of linear normalization. Staging accuracy rates of the former were 67. 27%, 80.26% and 79.41% for F0 versus F3, F2 versus F4 and F3 versus F4, respectively, secondary to none of the other classifiers. Conclusions: The assessment of equilibrium phase MR images with CAD can effectively stage rat liver fibrosis. This article highlights progress in developing a reliable non-invasive liver fibrosis staging procedure.

show abstract

Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

Cited by 4 publications

References 5 publications

Cirrhosis Classification Based on Texture Classification of Random Features

Cirrhosis Classification Based on Texture Classification of Random Features

Selection of Optimal Shape Features for Staging Hepatic Fibrosis on CT Image

Staging liver fibrosis by analysis of non-linear normalization texture in gadolinium-enhanced magnetic resonance imaging

Contact Info

Product

Resources

About