Unsupervised classification of cirrhotic livers using MRI data

Kanematsu, Masayuki; Kato, Hiroki; Kondo, Hiroshi; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Hoshi, Hiroaki

doi:10.1117/12.770862

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…Another significant approach refers to the combination between the supervised and unsupervised classification methods, the purpose being to detect the HCC tumor in incipient phase, based on histological features [18]. Regarding the unsupervised classification of the disease evolution stages, the authors performed in [19] the detection of the cirrhosis severity grades based on Magnetic Resonance Images (MRI). For this purpose, they employed textural features derived using the finite differences of the image intensity function and also an improved k-means clustering method.…”

Section: The State Of the Artmentioning

confidence: 99%

Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images

Mitrea

Nedevschi

Platon-Lupsor

et al. 2016

European Project Space on Intelligent Technologies, Software Engineering, Computer Vision, Graphics, Optics and Photonics

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This chapter describes specific, texture-based methods for the detection, characterization and recognition of some severe affections and of their evolution phases, using only information from ultrasound images. We perform the recognition of the considered affections in supervised manner, and we also discover the disease evolution phases in unsupervised manner. In both cases, the imagistic textural model is defined, consisting of: the relevant features for the characterization of the disease, respectively of its evolution phase; the specific values of the relevant textural features: arithmetic mean, standard deviation, probability distribution. Advanced texture analysis techniques, consisting of textural microstructure co-occurrence matrices based on Laws' features, are involved in this process. At the end, the imagistic textural model is validated through powerful, supervised classifiers, the resulting accuracy being around 90%.

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Section: The State Of the Artmentioning

confidence: 99%

Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images

Mitrea

Nedevschi

Platon-Lupsor

et al. 2016

European Project Space on Intelligent Technologies, Software Engineering, Computer Vision, Graphics, Optics and Photonics

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“…Liver cirrhosis is one of the leading causes of death by disease [ 1 ]. Making a definite diagnosis and staging of the cirrhosis is crucial which will help doctor to offer the timely and appropriate therapeutic method.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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“…Concerning the methods developed in the domain of medical image analysis and recognition for this purpose, several techniques were defined in order to distinguish cirrhotic and non-cirrhotic liver [3], [4], [5]. There not exist significant studies concerning the cirrhosis grading, through computerized methods, based on medical images.…”

Section: Introductionmentioning

confidence: 99%

“…The most relevant textural features in this situation were the entropy and the gradient non-zeros. In [4] the authors investigated the possibility to use an unsupervised classifier in order to distinguish between cirrhotic and non-cirrhotic patients. The accuracy of the classification rose to 72% sensitivity and 60% specificity.…”

Section: Introductionmentioning

confidence: 99%

Discovering the cirrhosis grades from ultrasound images by using textural features and clustering methods

Mitrea

Lupşor

Nedevschi

et al. 2013

2013 36th International Conference on Telecommunications and Signal Processing (TSP)

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Cirrhosis characterization and grading is an important issue nowadays in the medical domain, as this disease can lead to death. We aim to discover the cirrhosis grades in a noninvasive manner, using computerized methods. Concerning the feature computation, we chose the texture-based methods, as they revealed subtle aspects of the tissue, not detectable by the human eye. For this purpose, we used first, second and third order statistics of the gray levels, edge-based statistics, statistics of the textural microstructures, and also textural features computed at multiple resolutions, after applying the Wavelet transform. All these features were inputs to clustering methods, such as k-means clustering and expectation maximization (EM), implemented for the determination of the cirrhosis grades, each grade corresponding to a certain cluster. The relevant textural features, for each discovered grade, were also identified, by computing a specific score, for each feature, based on the result of the clustering methods.

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Unsupervised classification of cirrhotic livers using MRI data

Cited by 5 publications

References 16 publications

Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images

Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images

Cirrhosis Classification Based on Texture Classification of Random Features

Discovering the cirrhosis grades from ultrasound images by using textural features and clustering methods

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