Staging of the liver fibrosis from CT images using texture features

Kayaalti, Omer; Aksebzeci, Bekir Hakan; Karahan, İbrahim; Denız, Kemal; Ozturk, Menmet; Yılmaz, Bülent; Kara, Sadık; Asyalı, Musa Hakan

doi:10.1109/hibit.2012.6209041

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…The limitation with this approach is that the classification accuracy of complex lung tissue category is low. Kayaalti et al [10] proposed a noninvasive and fast approach to specify fibrosis using texture properties. Omer used gray-level co-occurrence matrix, discrete wavelet transform, and discrete Fourier transforms for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Texture Feature Extraction Using MGRLBP Method for Medical Image Classification

Ramamoorthy¹,

Kirubakaran²,

Ramalingam³

2014

Advances in Intelligent Systems and Computing

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Texture is an important significant property of medical images based on which images can be characterized and classified in a content-based image retrieval and classification system. This paper examines the feature extraction methods to ameliorate texture recognition accuracy by extracting the rotation-invariant texture feature from liver images by the individual Gabor filter method and by multi-scale Gabor rotation-invariant LBP (MGRLBP) method. The features extracted from both the approaches are tested on a set of 60 liver images of four different classes. The classification algorithms such as support vector machine (SVM) and k-nearest neighbor (KNN) were used to evaluate the extracted features from both methods, showing advancing improvements with the MGRLBP method over the individual method in the classification task.

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Section: Related Workmentioning

confidence: 99%

Texture Feature Extraction Using MGRLBP Method for Medical Image Classification

Ramamoorthy¹,

Kirubakaran²,

Ramalingam³

2014

Advances in Intelligent Systems and Computing

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“…Other studies showed that CTTA could be used successfully on routinely performed portal venous phase scans for the characterization of incidentally detected liver fibrosis (Table 1.). The initial research was conducted on either a single cross-section of the liver [6,7] or on small cuboid regions of interests [8][9][10]. Threedimensional texture analysis has only become available recently for describing cirrhosis-related changes in all three dimensions of the liver volume [11].…”

Section: Non-oncological Applications Of Cttamentioning

confidence: 99%

CT texture analysis of abdominal lesions – Part I.: Liver lesions

Budai

Frank

Shariati

et al. 2021

Imaging

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Artificial Intelligence and the use of radiomics analysis have been of great interest in the last decade in the field of imaging. CT texture analysis (CTTA) is a new and emerging field in radiomics, which seems promising in the assessment and diagnosis of both focal and diffuse liver lesions. The utilization of CTTA has only been receiving great attention recently, especially for response evaluation and prognostication of different oncological diagnoses. Radiomics, combined with machine learning techniques, offers a promising opportunity to accurately detect or differentiate between focal liver lesions based on their unique texture parameters. In this review article, we discuss the unique ability of radiomics in the diagnostics and prognostication of both focal and diffuse liver lesions. We also provide a brief review of radiogenomics and summarize its potential role of in the non-invasive diagnosis of malignant liver tumors.

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“…15 Kayaalti et al worked on liver fibrosis images to evaluate GLCM, discrete wavelet transform (DWT), and discrete Fourier transform (DFT). 16 Punia and Singh worked on the automatic detection of the liver by evaluated FOS and wavelet transform. 17 Bhuvaneswari et al worked on lung disease to detect them by features through moment invariants.…”

Section: Introductionmentioning

confidence: 99%

“…26 Kayaalti et al classified using k-NN and SVM. 16 Veeramuthu and Meenakshi classified the diseases using a decision tree, k-NN and Naive Byes classifiers. 19 Veeramuthu et al classified using k-NN, SVM, and Hybrid technique.…”

Section: Introductionmentioning

confidence: 99%

Ensembled liver cancer detection and classification using CT images

Krishan

Mittal

2020

Proc Inst Mech Eng H

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Computed tomography (CT) images are commonly used to diagnose liver disease. It is sometimes very difficult to comment on the type, category and level of the tumor, even for experienced radiologists, directly from the CT image, due to the varying intensities. In recent years, it has been important to design and develop computer-assisted imaging techniques to help doctors/physicians improve their diagnosis. The proposed work is to detect the presence of a tumor region in the liver and classify the different stages of the tumor from CT images. CT images of the liver have been classified between normal and tumor classes. In addition, CT images of the tumor have been classified between Hepato Cellular Carcinoma (HCC) and Metastases (MET). The performance of six different classifiers was evaluated on different parameters. The accuracy achieved for different classifiers varies between 98.39% and 100% for tumor identification and between 76.38% and 87.01% for tumor classification. To further, improve performance, a multi-level ensemble model is developed to detect a tumor (liver cancer) and to classify between HCC and MET using features extracted from CT images. The k-fold cross-validation (CV) is also used to justify the robustness of the classifiers. Compared to the individual classifier, the multi-level ensemble model achieved high accuracy in both the detection and classification of different tumors. This study demonstrates automated tumor characterization based on liver CT images and will assist the radiologist in detecting and classifying different types of tumors at a very early stage.

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Staging of the liver fibrosis from CT images using texture features

Cited by 9 publications

References 19 publications

Texture Feature Extraction Using MGRLBP Method for Medical Image Classification

Texture Feature Extraction Using MGRLBP Method for Medical Image Classification

CT texture analysis of abdominal lesions – Part I.: Liver lesions

Ensembled liver cancer detection and classification using CT images

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