Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection

Wang, Shitong; Chung, Fu-Lai; Fu, Duan

doi:10.1016/j.neucom.2006.07.012

Cited by 50 publications

(8 citation statements)

References 11 publications

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“…for i = 1, 2,..., n; j = 1, 2,..., m. For model (26), it is easy to obtain the following result: {1, γ ik ,γ jk } , k N , then system (26) has exactly one globally exponentially stable equilibrium point, and its exponential convergence rate equals l -μ.…”

Section: Exponential Stability and Exponential Convergence Ratementioning

confidence: 99%

“…al [19,20], many researchers have done extensive works on this subject due to their extensive applications in classification of image processing and pattern recognition. Specially, in the past few years, the stability analysis on FCNNs with various delays and fuzzy BAM neural networks with transmission delays has been the highlight in the neural network field, for example, see [21][22][23][24][25][26][27] and references therein. On the other hand, in respect of the complexity, besides delay effect, impulsive effect likewise exists in a wide variety of evolutionary processes in which states are changed abruptly at certain moments of time, involving such fields as medicine and biology, economics, mechanics, electronics and telecommunications.…”

Section: Introductionmentioning

confidence: 99%

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Integro-differential inequality and stability of BAM FCNNs with time delays in the leakage terms and distributed delays

Zhang

2011

J Inequal Appl

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In this paper, a class of impulsive bidirectional associative memory (BAM) fuzzy cellular neural networks (FCNNs) with time delays in the leakage terms and distributed delays is formulated and investigated. By establishing an integro-differential inequality with impulsive initial conditions and employing M-matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM FCNNs with time delays in the leakage terms and distributed delays are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on the delay kernel functions and system parameters. It is believed that these results are significant and useful for the design and applications of BAM FCNNs. An example is given to show the effectiveness of the results obtained here.

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Section: Exponential Stability and Exponential Convergence Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integro-differential inequality and stability of BAM FCNNs with time delays in the leakage terms and distributed delays

Zhang

2011

J Inequal Appl

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“…White blood cells (WBCs) [1], [2] are important defense cells in human blood that consists of five kinds of cells, i.e., neutrophils, basophils, eosinophils, monocytes, and lymphocytes. The WBC segmentation is a challenging task for a variety of medical diagnosis applications.…”

Section: Introductionmentioning

confidence: 99%

White Blood Cell Segmentation via Sparsity and Geometry Constraints

et al. 2019

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Digital pathology and microscopic image analysis play an important role in cell morphology research. In particular, the effective segmentation of White Blood Cells (WBCs) remains a challenging problem due to the blurring boundaries of WBCs under rapid staining, as well as the adhesion between leukocytes and other cells. In this paper, we propose a novel WBC (including nuclei and cells) segmentation algorithm based on both sparsity and geometry constraints. Specifically, we first construct a sparse image representation via combining the HSL color space and the RGB color channels, followed by the use of a sparsity constraint to only preserve useful information from the nuclei features. In addition, we introduce a robust model fitting strategy (i.e., the geometry constraint) to detect cells. Our model fitting strategy is able to significantly improve the robustness of the proposed segmentation algorithm against outliers that could seriously contaminate WBCs. The experimental results show that the proposed algorithm presents clear advantages over the state-of-the-art WBC segmentation algorithms in terms of accuracy. INDEX TERMS Geometry constraint, sparsity constraint, white blood cell segmentation.

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“…(2006) proposed a method based on multispectral imaging technique for WBC segmentation. Hitong et al (2007) applied fuzzy cellular neural network (IFCNN) to leukocyte segmentation. A computer assisted method for leukocyte segmentation was proposed by Huang et.…”

Section: Introductionmentioning

confidence: 99%

Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding

Jati¹,

Singh²,

Mukherjee³

et al. 2014

Micron

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Abstract-The paper proposes a robust approach to automatic segmentation of leukocyte"s nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert haematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.

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Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection

Cited by 50 publications

References 11 publications

Integro-differential inequality and stability of BAM FCNNs with time delays in the leakage terms and distributed delays

Integro-differential inequality and stability of BAM FCNNs with time delays in the leakage terms and distributed delays

White Blood Cell Segmentation via Sparsity and Geometry Constraints

Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding

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