Texture analysis of liver cirrhosis

Kayaalti, Omer; Aksebzeci, Bekir Hakan; Asyalı, Musa Hakan; Karahan, Okan; Denız, Kemal; Öztürk, Mehmet

doi:10.1109/biyomut.2010.5479742

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…The research group of Gifu University [4], [5] and Kayaalti et al [6] used texture features as input and classified normal/cirrhotic livers by using, respectively, an artificial neural network (ANN) and a support vector machine (SVM). Both groups obtained promising results.…”

Section: Introductionmentioning

confidence: 99%

Sparse and Low-Rank Matrix Decomposition for Local Morphological Analysis to Diagnose Cirrhosis

Deng

Han

Chen

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYChronic liver disease is a major worldwide health problem. Diagnosis and staging of chronic liver diseases is an important issue. In this paper, we propose a quantitative method of analyzing local morphological changes for accurate and practical computer-aided diagnosis of cirrhosis. Our method is based on sparse and low-rank matrix decomposition, since the matrix of the liver shapes can be decomposed into two parts: a low-rank matrix, which can be considered similar to that of a normal liver, and a sparse error term that represents the local deformation. Compared with the previous global morphological analysis strategy based on the statistical shape model (SSM), our proposed method improves the accuracy of both normal and abnormal classifications. We also propose using the norm of the sparse error term as a simple measure for classification as normal or abnormal. The experimental results of the proposed method are better than those of the state-of-the-art SSM-based methods. key words: local morphological analysis, diagnosis of cirrhosis, sparse and low-rank matrix decomposition, augmented lagrange multiplier

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Section: Introductionmentioning

confidence: 99%

Sparse and Low-Rank Matrix Decomposition for Local Morphological Analysis to Diagnose Cirrhosis

Deng

Han

Chen

et al. 2014

IEICE Trans. Inf. & Syst.

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“…The research group of Gifu University [4] [5] and Kayaalti et al [6] used texture features as input; they classified normal/cirrhotic liver by Artificial Neural Network (ANN) and Support Vector Machine (SVM), respectively. Both of them obtained high accuracy classification result.…”

Section: Introductionmentioning

confidence: 99%

Sparse and Low Rank Matrix Decomposition Based Local Morphological Analysis and Its Application to Diagnosis of Cirrhosis Livers

Deng

Han

et al. 2014

2014 22nd International Conference on Pattern Recognition

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Cirrhosis liver is a terrible disease which is threatening our lives. Meanwhile, cirrhosis will cause significant hepatic morphological changes. While it is well known that the livers from different subjects have similar global shape structure which means liver shape ensemble should be low-rank. However the deformation which caused by cirrhosis can be considered as sparse compared with the whole liver. Therefore, in this study, we proposed to apply spare and low-rank matrix decomposition to partition the local deformation part (sparse error matrix E) from the global similar structure (low-rank matrix A) using the input liver shape D, which is the landmark coordinates of liver shapes and already have been aligned by the current rigid registration methods firstly. And then sparse matrix E is used for diagnosis. In common sense,the normal liver should have less local deformation than that of abnormal liver, which means that the norm of sparse matrix E for normal liver is smaller than the norm for abnormal one. Thus, we can simply use a threshold classify normal and abnormal livers using the norm of E for these two categories. The proposed method is evaluated by a liver database which includes 30 normal livers and 30 abnormal livers. The experimental results of proposed method is better than those of state of the art statistical shape model(SSM) based methods.

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“…Wang et al [4] used the texture analyze with co-occurrence matrix method to analyze ultrasonograms of normal or diseased livers, although they proved the texture analysis can help cirrhosis diagnosis, the diagnostic accuracy was not yet satisfied. The research group of Gifu University [5][6] [7] and Kayaalti et al [8] used texture features as input, they classified normal/cirrhotic liver by Artificial Neural Network (ANN) and Support Vector Machine (SVM) respectively. Both of them obtained high accuracy classification result.…”

Section: Introductionmentioning

confidence: 99%