Advanced fuzzy cellular neural network: Application to CT liver images

Wang, Shitong; Fu, Duan; Xu, Min; Hu, Dewen

doi:10.1016/j.artmed.2006.08.001

Cited by 68 publications

(11 citation statements)

References 14 publications

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“…In the same year, Zhang in [23] gave more general results. They proved that the kernelized version of a learning algorithm can be implemented by performing the learning algorithm with the transformed data by the full-rank KPCA, if the learning algorithm satisfies the following two mild conditions simultaneously: (1) the output result of the learning [3,[15][16][17], Positive and negative fuzzy rule systems, see [15] Activation functions with kernels [3,18,19] 1) Most frequently used sigmodial functions such as…”

Section: Remarkmentioning

confidence: 99%

Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Wang

Jiang

Chung

et al. 2015

Applied Soft Computing

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Section: Remarkmentioning

confidence: 99%

Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Wang

Jiang

Chung

et al. 2015

Applied Soft Computing

Self Cite

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“…However, it is easy to extend the result to the T-S FCNNs with parametric uncertainties without loss of generality. In papers [12][13][14][15][16][17][18][19], the fuzzy AND and fuzzy OR operation makes the analysis complex, which are very restrictive and limit their applications. In future we will consider information of leakage term to FCNNs discussed in [14][15][16][17][18][19] and it is quite obvious.…”

Section: Remark 46mentioning

confidence: 99%

Leakage Delays in T–S Fuzzy Cellular Neural Networks

Balasubramaniam

Vembarasan

Rakkiyappan

2011

Neural Process Lett

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In this paper, the Takagi-Sugeno (T-S) fuzzy model representation is extended to the stability analysis for cellular neural networks (CNNs) with mixed time-varying delays and time delay in the leakage term via the delay decomposition approach. First, a sufficient condition is given to ensure the existence and uniqueness of equilibrium point by using topological degree theory. Then, we present global asymptotic stability of equilibrium point by using linear matrix inequality (LMI) approach and by constructing an augmented LyapunovKrasovskii functional (ALKF) together with convex combination method. The proposed results can be easily solved by some standard numerical packages. Finally, four numerical examples are given to demonstrate the effectiveness and conservativeness of our proposed results.

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“…Goryawala et al 12 propose a semiautomatic algorithm that is structured as a parallel-aware process so that a computationally taxing task can be distributed over various computing nodes. Wang et al 13 propose a novel fuzzy cellular neural-network based method addressing problems like uneven liver boundaries and the overlapping of organs. Liu et al 14 develop a concavity removal algorithm that deals with abnormal livers, particularly with those having lesions at the liver edges.…”

Section: Related Workmentioning

confidence: 99%

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

2015

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Abstract. Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computerassisted interventions grand challenge workshop. Various parameters in the algorithm, such as w , Δt , z, α, μ, α 1 , and α 2 , play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.

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Advanced fuzzy cellular neural network: Application to CT liver images

Cited by 68 publications

References 14 publications

Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Leakage Delays in T–S Fuzzy Cellular Neural Networks

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

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